Methods and apparatus for calibrating spectral data

[0001] This application is related to the following commonly-owned applications: Ser. No. 10/418,668, entitled, “Methods and Apparatus for Characterization of Tissue Samples”; Ser. No. 10/418,902, entitled, “Methods and Apparatus for Displaying Diagnostic Data”; Ser. No. 10/418,974, entitled, “Methods and Apparatus for Visually Enhancing Images”; Ser. No. 10/418,922, entitled, “Methods and Apparatus for Characterization of Tissue Samples”; Ser. No. 10/418,975, entitled, “Methods and Apparatus for Processing Image Data for Use in Tissue Characterization”; Ser. No. 10/418,415, entitled, “Methods and Apparatus for Processing Spectral Data for Use in Tissue Characterization”; and Ser. No. 10/419,181, entitled, “Methods and Apparatus for Evaluating Image Focus,” all of which are filed on even date herewith.

[0002] This invention relates generally to spectroscopic methods. More particularly, in certain embodiments, the invention relates to methods of calibrating spectral data for use in a tissue classification scheme.

[0003] It is common in the field of medicine to perform visual examination to diagnose disease. For example, visual examination of the cervix can discern areas where there is a suspicion of pathology. However, direct visual observation alone may be inadequate for proper identification of an abnormal tissue sample, particularly in the early stages of disease.

[0004] In some procedures, such as colposcopic examinations, a chemical agent, such as acetic acid, is applied to enhance the differences in appearance between normal and pathological tissue. Such acetowhitening techniques may aid a colposcopist in the determination of areas in which there is a suspicion of pathology.

[0005] Colposcopic techniques are not perfect. They generally require analysis by a highly-trained physician. Colposcopic images may contain complex and confusing patterns and may be affected by glare, shadow, or the presence of blood or other obstruction, rendering an indeterminate diagnosis.

[0006] Spectral analysis has increasingly been used to diagnose disease in tissue. Spectral analysis is based on the principle that the intensity of light that is transmitted from an illuminated tissue sample may indicate the state of health of the tissue. As in colposcopic examination, spectral analysis of tissue may be conducted using a contrast agent such as acetic acid. In spectral analysis, the contrast agent is used to enhance differences in the light that is transmitted from normal and pathological tissues.

[0007] Spectral analysis offers the prospect of at least partially-automated diagnosis of tissue using a classification algorithm. However, current methods of calibrating spectral data acquisition systems do not provide sufficient accuracy or repeatability needed for pinpoint tissue diagnosis. Current calibration methods do not adequately account for stray light effects, chromatic aberrations, and spatial inhomogeneities. Furthermore, areas of disease may be interspersed among neighboring healthy tissue, rendering overly-diffuse, improperly-calibrated spectral data.

[0008] There exists a general need for more accurate and more precise calibration methods for spectral data acquisition systems.

[0009] The invention provides methods for calibrating spectral data acquisition systems. These calibration methods produce spectral data sufficiently accurate for use in tissue classification algorithms. More specifically, the invention improves the accuracy of spectral-based tissue classification schemes, in part, by properly accounting for spatial variations, instrument-to-instrument variations, and patient-to-patient variations in the acquisition of spectral data from tissue samples.

[0010] The invention provides systems for diagnoses of tissue samples. A single spectral scan may consist of the acquisition of spectral data from each of about 500 regions of a tissue sample with centers spaced about 1.1 mm apart. Other spacings may be used with the same, fewer, or more regions. Proper calibration is necessary to provide a baseline standard for the test spectral data obtained, as well as for the reference spectral data upon which the diagnostic classification schemes are based. Differences in spectral data from a tissue sample should be attributable to the tissue itself, not to baseline variations. Baseline variations may be caused, for example, by stray light effects, electronic background effects, variation in light energy delivered to a tissue sample, spatial heterogeneities of the illumination source, chromatic aberrations of the scanning optics, variation in wavelength response of the collection optics, and the efficiency of the collection optics.

[0011] Therefore, a preferred method of the invention comprises obtaining calibration data from spaced-apart locations on a reference target, wherein the locations are keyed to locations of a tissue sample from which spectral data are subsequently obtained. The reference target may be, for example, a solid target having a known reflectance, a fluorescent dye-filled target, a disposable target placed directly onto a tissue sample, or an “open air” target. Moreover, a preferred method of the invention comprises processing spectral data from a tissue sample using calibration data obtained as part of the routine preventive maintenance of an optical instrument, as well as using calibration data obtained under test conditions just prior to a patient scan. An initial, “factory” calibration of the instrument may be performed additionally, or in place of the preventive maintenance calibration. A calibration baseline may thus be obtained at each region of a tissue sample using calibration data obtained under tightly controlled preventive maintenance and/or factory conditions, as well as using calibration data obtained under pre-patient conditions, which more closely approximate actual test conditions. The preventive maintenance and/or factory calibrations account for instrument-to-instrument variability, which is particularly important in building a reference spectral database containing data obtained from a number of individual instruments. Thus, methods of the invention comprise adjusting for individual instrument response and correcting for temporal, patient-to-patient variability.

[0012] Before obtaining a spectral scan during a patient exam, a single-use disposable sheath may be placed on a part of the optical instrument that comes into contact with patient tissue. The disposable sheath may affect the spectral data obtained. Thus, one embodiment of the invention comprises obtaining calibration data in a pre-patient test performed with a disposable sheath in place, under patient scan conditions. In order to account for instrument-to-instrument variability, the embodiment further comprises obtaining factory and/or preventive maintenance calibration data with a disposable sheath in place, as in the pre-patient test. The disposables used in the factory and/or preventive maintenance tests are of the same type as the disposables used in the patient scans, but they are typically not the same individual disposables, which should generally be maintained under sterile conditions. This difference is accounted for using calibration algorithms of the invention.

[0013] Furthermore, methods of the invention provide procedures for dealing with internal stray light effects. Internal stray light includes cross-talk between transmitted light and collection optics of a spectral data acquisition system. Typically, light is produced and transmitted internally within an optical instrument. The internal light is generally shielded from the collection optics so that light collected from the sample is not thereby contaminated. However, there is generally some internal stray light that affects spectral readings from tissue samples. Preferred methods of the invention comprise correcting for internal stray light by obtaining spaced-apart data from a “null” target of sufficiently low reflectance so as to yield a residual optical signal under patient scan conditions. Preferred methods additionally comprise obtaining spaced-apart data from a factory or preventive-maintenance test performed in “open air” (with no target) and in the absence of external light (e.g., in a darkroom). Then, spectral data obtained from a patient scan are calibrated using data from the “null target” and “open air” tests.

[0014] Accordingly, the invention comprises obtaining calibration data from a plurality of spaced-apart locations of a calibration target, obtaining spectral data from a tissue sample at regions corresponding to the spaced-apart locations, and calibrating the spectral data using the calibration data. Methods of the invention also comprise obtaining calibration data from spaced-apart locations of a calibration target using an optical instrument with a disposable sheath, and using the data to calibrate subsequently-obtained spectral data from a tissue sample. Moreover, the invention comprises correcting spectral data for internal stray light effects by performing “null target” and “open air” tests.

• [0162] System overview
• [0163] Instrument
• [0164] Spectral calibration
• [0165] Patient scan procedure
• [0166] Video calibration and focusing
• [0167] Determining optimal data acquisition window
• [0168] Motion tracking
• [0169] Broadband reflectance arbritration and low-signal masking
• [0170] Classification system overview
• [0171] Spectral masking
• [0172] Image masking
  • [0173] Glare_vid
  • [0174] [ROI]_vid
  • [0175] [ST]_vid
  • [0176] Os_vid
  • [0177] Blood_vid
  • [0178] Mucus_vid
  • [0179] [SP]_vid
  • [0180] [VW]_vid
  • [0181] [FL]_vid
• [0182] Classifiers
• [0183] Combining spoectral and image data
• [0184] Image enhancement
• [0185] Diagnostic display

[0186] The Table of Contents above is provided as a general organizational guide to the Description of the Illustrative Embodiment. Entries in the Table do not serve to limit support for any given element of the invention to a particular section of the Description.

[0187] The invention provides systems and methods for obtaining spectral data and image data from a tissue sample, for processing the data, and for using the data to diagnose the tissue sample. As used herein, “spectral data” from a tissue sample includes data corresponding to any wavelength of the electromagnetic spectrum, not just the visible spectrum. Where exact wavelengths are specified, alternate embodiments comprise using wavelengths within a ±5 nm range of the given value, within a ±10 nm range of the given value, and within a ±25 nm range of the given value. As used herein, “image data” from a tissue sample includes data from a visual representation, such as a photo, a video frame, streaming video, and/or an electronic, digital or mathematical analogue of a photo, video frame, or streaming video. As used herein, a “tissue sample” may comprise, for example, animal tissue, human tissue, living tissue, and/or dead tissue. A tissue sample may be in vivo, in situ, ex vivo, or ex situ, for example. A tissue sample may comprise material in the vacinity of tissue, such as non-biological materials including dressings, chemical agents, and/or medical instruments, for example.

[0188] Embodiments of the invention include obtaining data from a tissue sample, determining which data are of diagnostic value, processing the useful data to obtain a prediction of disease state, and displaying the results in a meaningful way. In one embodiment, spectral data and image data are obtained from a tissue sample and are used to create a diagnostic map of the tissue sample showing regions in which there is a high probability of disease.

[0189] The systems and methods of the invention can be used to perform an examination of in situ tissue without the need for excision or biopsy. In an illustrative embodiment, the systems and methods are used to perform in-situ examination of the cervical tissue of a patient in a non-surgical setting, such as in a doctor's office or examination room. The examination may be preceded or accompanied by a routine pap smear and/or colposcopic examination, and may be followed-up by treatment or biopsy of suspect tissue regions.

[0190] FIG. 1 depicts a block diagram featuring components of a tissue characterization system 100 according to an illustrative embodiment of the invention. Each component of the system 100 is discussed in more detail herein. The system includes components for acquiring data, processing data, calculating disease probabilities, and displaying results.

[0191] In the illustrative system 100 of FIG. 1, an instrument 102 obtains spectral data and image data from a tissue sample. The instrument 102 obtains spectral data from each of a plurality of regions of the sample during a spectroscopic scan of the tissue 104. During a scan, video images of the tissue are also obtained by the instrument 102. Illustratively, one or more complete spectroscopic spectra are obtained for each of 500 discrete regions of a tissue sample during a scan lasting about 12 seconds. However, in other illustrative embodiments any number of discrete regions may be scanned and the duration of each scan may vary. Since in-situ tissue may shift due to involuntary or voluntary patient movement during a scan, video images are used to detect shifts of the tissue, and to account for the shifts in the diagnostic analysis of the tissue. Preferably, a detected shift is compensated for in real time 106. For example, as described below in further detail, one or more components of the instrument 102 may be automatically adjusted during the examination of a patient while spectral data are obtained in order to compensate for a detected shift caused by patient movement. Additionally or alternatively, the real-time tracker 106 provides a correction for patient movement that is used to process the spectral data before calculating disease probabilities. In addition to using image data to track movement, the illustrative system 100 of FIG. 1 uses image data to identify regions that are obstructed or are outside the areas of interest of a tissue sample 108. This feature of the system 100 of FIG. 1 is discussed herein in more detail.

[0192] The system 100 shown in FIG. 1 includes components for performing factory tests and periodic preventive maintenance procedures 110, the results of which 112 are used to preprocess patient spectral data 114. In addition, reference spectral calibration data are obtained 116 in an examination setting prior to each patient examination, and the results 118 of the pre-patient calibration are used along with the factory and preventive maintenance results 112 to preprocess patient spectral data 114.

[0193] The instrument 102 of FIG. 1 includes a frame grabber 120 for obtaining a video image of the tissue sample. A focusing method 122 is applied and video calibration is performed 124. The corrected video data may then be used to compensate for patient movement during the spectroscopic data acquisition 104. The corrected video data is also used in image masking 108, which includes identifying obstructed regions of the tissue sample, as well as regions of tissue that lie outside an area of diagnostic interest. In one illustrative embodiment, during a patient scan, a single image is used to compute image masks 108 and to determine a brightness and contrast correction 126 for displaying diagnostic results. In illustrative alternative embodiments, more than one image is used to create image masks and/or to determine a visual display correction.

[0194] In the system of FIG. 1, spectral data are acquired 104 within a predetermined period of time following the application of a contrast agent, such as acetic acid, to the tissue sample. According to the illustrative embodiment, four raw spectra are obtained for each of approximately 500 regions of the tissue sample and are processed. A fluorescence spectrum, two broadband reflectance (backscatter) spectra, and a reference spectrum are obtained at each of the regions over a range from about 360 nm to about 720 nm wavelength. The period of time within which a scan is acquired is chosen so that the accuracy of the resulting diagnosis is maximized. In one illustrative embodiment, a spectral data scan of a cervical tissue sample is performed over an approximately 12-second period of time within a range between about 30 seconds and about 130 seconds following application of acetic acid to the tissue sample.

[0195] The illustrative system 100 includes data processing components for identifying data that are potentially non-representative of the tissue sample. Preferably, potentially non-representative data are either hard-masked or soft-masked. Hard-masking of data includes eliminating the identified, potentially non-representative data from further consideration. This results in an indeterminate diagnosis in the corresponding region. Hard masks are determined in components 128, 130, and 108 of the system 100. Soft masking includes applying a weighting function or weighting factor to the identified, potentially non-representative data. The weighting is taken into account during calculation of disease probability 132, and may or may not result in an indeterminate diagnosis in the corresponding region. Soft masks are determined in component 130 of the system 100.

[0196] Soft masking provides a means of weighting spectral data according to the likelihood that the data is representative of clear, unobstructed tissue in a region of interest. For example, if the system 100 determines there is a possibility that one kind of data from a given region is affected by an obstruction, such as blood or mucus, that data is “penalized” by attributing a reduced weighting to that data during calculation of disease probability 132. Another kind of data from the same region that is determined by the system 100 not to be affected by the obstruction is more heavily weighted in the diagnostic step than the possibly-affected data, since the unaffected data is attributed a greater weighting in the calculation of disease probability 132.

[0197] In the illustrative system 100, soft masking is performed in addition to arbitration of two or more redundant data sets. Arbitration of data sets is performed in component 128. In the illustrative embodiment, this type of arbitration employs the following steps: obtaining two sets of broadband reflectance (backscatter) data from each region of the tissue sample using light incident to the region at two different angles; determining if one of the data sets is affected by an artifact such as shadow, glare, or obstruction; eliminating one of the redundant reflectance data sets so affected; and using the other data set in the diagnosis of the tissue at the region. If both of the data sets are unaffected by an artifact, a mean of the two sets is used.

[0198] According to the illustrative embodiment, the instrument 102 obtains both video images and spectral data from a tissue sample. The spectral data may include fluorescence data and broadband reflectance (backscatter) data. The raw spectral data are processed and then used in a diagnostic algorithm to determine disease probability for regions of the tissue sample. According to the illustrative embodiment, both image data and spectral data are used to mask data that is potentially non-representative of unobstructed regions of interest of the tissue. In another illustrative embodiment, both the image data and the spectral data are alternatively or additionally used in the diagnostic algorithm.

[0199] The system 100 also includes a component 132 for determining a disease probability at each of a plurality of the approximately 500 interrogation points using spectral data processed in the components 128 and 130 and using the image masks determined in component 108. Illustratively, the disease probability component 132 processes spectral data with statistical and/or heuristics-based (non-statistically-derived) spectral classifiers 134, incorporates image and/or spectral mask information 136, and assigns a probability of high grade disease, such as CIN 2+, to each examined region of the tissue sample. The classifiers use stored, accumulated training data from samples of known disease state. The disease display component 138 graphically presents regions of the tissue sample having the highest probability of high grade disease by employing a color map overlay of the cervical tissue sample. The disease display component 138 also displays regions of the tissue that are necrotic and/or regions at which a disease probability could not be determined.

[0200] Each of the components of the illustrative system 100 is described in more detail below.

[0201] FIG. 2 is a schematic representation of components of the instrument 102 used in the tissue characterization system 100 of FIG. 1 to obtain spectral data and image data from a tissue sample according to an illustrative embodiment of the invention. The instrument of FIG. 2 includes a console 140 connected to a probe 142 by way of a cable 144. The cable 144 carries electrical and optical signals between the console 140 and the probe 142. In an alternative embodiment, signals are transmitted between the console 140 and the probe 142 wirelessly, obviating the need for the cable 144. The probe 142 accommodates a disposable component 146 that comes into contact with tissue and may be discarded after one use. The console 140 and the probe 142 are mechanically connected by an articulating arm 148, which can also support the cable 144. The console 140 contains much of the hardware and the software of the system, and the probe 142 contains the necessary hardware for making suitable spectroscopic observations. The details of the instrument 100 are further explained in conjunction with FIG. 3.

[0202] FIG. 3 shows an exemplary operational block diagram 150 of an instrument 102 of the type depicted in FIG. 2. Referring to FIGS. 1 and 2, in some illustrative embodiments the instrument 102 includes features of single-beam spectrometer devices, but is adapted to include other features of the invention. In other illustrative embodiments, the instrument 102 is substantially the same as double-beam spectrometer devices, adapted to include other features of the invention. In still other illustrative embodiments the instrument 102 employs other types of spectroscopic devices. In the depicted embodiment, the console 140 includes a computer 152, which executes software that controls the operation of the instrument 102. The software includes one or more modules recorded on machine-readable media such as magnetic disks, magnetic tape, CD-ROM, and semiconductor memory, for example. Preferably, the machine-readable medium is resident within the computer 152. In alternative embodiments, the machine-readable medium can be connected to the computer 152 by a communication link. However, in alternative embodiments, one can substitute computer instructions in the form of hardwired logic for software, or one can substitute firmware (i.e., computer instructions recorded on devices such as PROMs, EPROMS, EEPROMs, or the like) for software. The term machine-readable instructions as used herein is intended to encompass software, hardwired logic, firmware, object code and the like.

[0203] The computer 152 of the instrument 102 is preferably a general purpose computer. The computer 152 can be, for example, an embedded computer, a personal computer such as a laptop or desktop computer, or another type of computer, that is capable of running the software, issuing suitable control commands, and recording information in real-time. The illustrative computer 152 includes a display 154 for reporting information to an operator of the instrument 102, a keyboard 156 for enabling the operator to enter information and commands, and a printer 158 for providing a print-out, or permanent record, of measurements made by the instrument 102 and for printing diagnostic results, for example, for inclusion in the chart of a patient. According to the illustrative embodiment of the invention, some commands entered at the keyboard 156 enable a user to perform certain data processing tasks, such as selecting a particular spectrum for analysis, rejecting a spectrum, and/or selecting particular segments of a spectrum for normalization. Other commands enable a user to select the wavelength range for each particular segment and/or to specify both wavelength contiguous and non-contiguous segments. In one illustrative embodiment, data acquisition and data processing are automated and require little or no user input after initializing a scan.

[0204] The illustrative console 140 also includes an ultraviolet (UV) source 160 such as a nitrogen laser or a frequency-tripled Nd:YAG laser, one or more white light sources 162 such as one, two, three, four, or more Xenon flash lamps, and control electronics 164 for controlling the light sources both as to intensity and as to the time of onset of operation and the duration of operation. One or more power supplies 166 are included in the illustrative console 140 to provide regulated power for the operation of all of the components of the instrument 102. The illustrative console 140 of FIG. 3 also includes at least one spectrometer and at least one detector (spectrometer and detector 168) suitable for use with each of the light sources. In some illustrative embodiments, a single spectrometer operates with both the UV light source 160 and the white light source(s) 162. The same detector may record both UV and white light signals. However, in other illustrative embodiments, different detectors are used for each light source.

[0205] The illustrative console 140 further includes coupling optics 170 to couple the UV illumination from the UV light source 160 to one or more optical fibers in the cable 144 for transmission to the probe 142, and coupling optics 172 for coupling the white light illumination from the white light source(s) 162 to one or more optical fibers in the cable 144 for transmission to the probe 142. The spectral response of a specimen to UV illumination from the UV light source 160 observed by the probe 142 is carried by one or more optical fibers in the cable 144 for transmission to the spectrometer and detector 168 in the console 140. The spectral response of a specimen to the white light illumination from the white light source(s) 162 observed by the probe 142 is carried by one or more optical fibers in the cable 144 for transmission to the spectrometer and detector 168 in the console 140. As shown in FIG. 3, the console 140 includes a footswitch 174 to enable an operator of the instrument 102 to signal when it is appropriate to commence a spectral scan by stepping on the switch. In this manner, the operator has his or her hands free to perform other tasks, for example, aligning the probe 142.

[0206] The console 140 additionally includes a calibration port 176 into which a calibration target may be placed for calibrating the optical components of the instrument 102. Illustratively, an operator places the probe 142 in registry with the calibration port 176 and issues a command that starts the calibration operation. In illustrative calibration operation, a calibrated light source provides a calibration signal in the form of an illumination of known intensity over a range of wavelengths, and/or at a number of discrete wavelengths. The probe 142 detects the calibration signal, and transmits the detected signal through the optical fiber in the cable 144 to the spectrometer and detector 168. A test spectral result is obtained. A calibration of the spectral system can be computed as the ratio of the amplitude of the known illumination at a particular wavelength divided by the test spectral result at the same wavelength. Calibration may include factory calibration 110, preventive maintenance calibration 110, and/or pre-patient calibration 116, as shown in the system 100 of FIG. 1. Pre-patient calibration 116 may be performed to account for patient-to-patient variation, for example.

[0207] FIG. 4 depicts the illustrative probe 142 of FIG. 2 resting within a calibration port 176 according to an illustrative embodiment of the invention. Referring to FIGS. 2-4, the illustrative calibration port 176 is adjustably attached to the probe 142 or the console 140 to allow an operator to perform pre-patient calibration without assembling detachable parts. The pre-patient calibration port may contain one or more pre-positioned calibration targets, such as a customized target 426 (see also FIG. 19) and a null,target 187, both described in more detail below.

[0208] According to the illustrative embodiment, factory and/or preventive maintenance calibration includes using a portable, detachable calibration port to calibrate any number of individual units, allowing for a standardized calibration procedure among various instruments. Preferably, the calibration port 176 is designed to prevent stray room light or other external light from affecting a calibration measurement when a calibration target is in place in the calibration port 176. For example, as shown in FIG. 4, the null target 187 can be positioned up against the probe head 192 by way of an actuator 189 such that the effect of external stray light is minimized. When not in use, the null target 187 is positioned out of the path of light between the customized target 426 and the collection optics 200, as depicted in FIG. 4. An additional fitting may be placed over the probe head 192 to further reduce the effect of external stray light. According to one illustrative embodiment, the target 187 in the calibration port 176 is located approximately 100 mm from the probe head 192; and the distance light travels from the target 187 to the first optical component of the probe 142 is approximately 130 mm. The location of the target (in relation to the probe head 192) during calibration may approximate the location of tissue during a patient scan.

[0209] The illustrative probe 142 includes probe optics 178 for illuminating a specimen to be analyzed with UV light from the UV source 160 and for collecting the fluorescent and broadband reflectance (backscatter) illumination from the specimen being analyzed. The illustrative probe 142 of FIGS. 2 and 3 includes a scanner assembly 180 that provides illumination from the UV source 160, for example, in a raster pattern over a target area of the specimen of cervical tissue to be analyzed. The probe 142 also includes a video camera 182 for observing and recording visual images of the specimen under analysis. The probe 142 also includes a targeting source 184 for determining where on the surface of the specimen to be analyzed the probe 142 is pointing. The probe 142 also includes white light optics 186 to deliver white light from the white light source(s) 162 for recording the reflectance data and to assist the operator in visualizing the specimen to be analyzed. Once the operator aligns the instrument 102 and depresses the footswitch 174, the computer 152 controls the actions of the light sources 160, 162, the coupling optics 170, 172, the transmission of light signals and electrical signals through the cable 144, the operation of the probe optics 178 and the scanner assembly 180, the retrieval of observed spectra, the coupling of the observed spectra into the spectrometer and detector 168 via the cable 144, the operation of the spectrometer and detector 168, and the subsequent signal processing and analysis of the recorded spectra.

[0210] FIG. 4 depicts the probe 142 having top and bottom illumination sources 188, 190 according to an illustrative embodiment of the invention. In this embodiment, the illumination sources 188, 190 are situated at an upper and a lower location about the perimeter of a probe head 192 such that there is illuminating light incident to a target area at each of two different angles. In one embodiment, the target area is a tissue sample. The probe head 192 contains probe optics 178 for illuminating regions of tissue and for collecting illumination reflected or otherwise emitted from regions of tissue. Illustratively, the probe optics for collecting the illumination 200 are located between the top and bottom illumination sources 188, 190. In other illustrative embodiments, other arrangements of the illuminating and collecting probe optics 178 are used that allow the illumination of a given region of tissue with light incident to the region at more than one angle. One such arrangement includes the collecting optics 200 positioned around the illuminating optics.

[0211] In one illustrative embodiment, the top and bottom illumination sources 188, 190 are alternately turned on and off in order to sequentially illuminate the tissue at equal and opposite angles relative to the collection axis. For example, the top illumination source 188 is turned on while the bottom illumination source 190 is turned off, such that spectral measurements may be obtained for light reflected from a region of the tissue sample 194 illuminated with light incident to the region at a first angle. This angle is relative to the surface of the tissue sample at a point on the region, for example. Then, the top illumination source 188 is turned off while the bottom illumination source 190 is turned on, such that spectral measurements may be obtained using light incident to the region at a second angle. If data obtained using one of the illumination sources is adversely affected by an artifact, such as glare or shadow, then data obtained using another illumination source, with light incident to the region at a different angle, may be unaffected by the artifact and may still be useful. The spectral measurements can include reflectance and/or fluorescence data obtained over a range of wavelengths.

[0212] According to the various illustrative embodiments, the top and the bottom illumination sources 188, 190 may be alternately cycled on and off more than once while obtaining data for a given region. Also, cycles of the illumination sources 188, 190 may overlap, such that more than one illumination source is on at one time for at least part of the illumination collection procedure. Other illumination alternation schemes are possible, depending at least in part on the arrangement of illumination sources 188, 190 in relation to the probe head 192.

[0213] After data are obtained from one region of the tissue using light incident to the region at more than one angle, data may likewise be obtained from another region of the tissue. In the illustrative embodiment of FIG. 4, the scanner assembly 180 illuminates a target area of the tissue sample region-by-region. Illustratively, a first region is illuminated using light incident to the region at more than one angle as described above, then the probe optics 178 are automatically adjusted to repeat the illumination sequence at a different region within the target area of the tissue sample. The illustrative process is repeated until a desired subset of the target area has been scanned. As mentioned above, preferably about five hundred regions are scanned within a target area having a diameter of about 25-mm. Using the instrument 102, the scan of the aforementioned five hundred regions takes about 12 seconds. In other illustrative embodiments, the number of regions scanned, the size of the target area, and/or the duration of the scan vary from the above.

[0214] FIG. 5 depicts an exemplary scan pattern 202 used by the instrument 102 to obtain spatially-correlated spectral data and image data from a tissue sample according to an illustrative embodiment of the invention. Illustratively, spectral data are obtained at 499 regions of the tissue sample, plus one region out of the field of view of the cervix obtained, for example, for calibration purposes. The exemplary scan pattern 202 of FIG. 5 includes 499 regions 204 whose centers are inside a circle 206 that measures about 25.8 mm in diameter. The center of each region is about 1.1 mm away from each of the nearest surrounding regions. This may be achieved by offsetting each scan line by about 0.9527 mm in the y-direction and by staggering each scan line in the x-direction by about 0.55 mm. Each of the 499 regions is about 0.7 mm in diameter. In other illustrative embodiments, other geometries are used.

[0215] According to the illustrative embodiment, the spectral data acquisition component 104 of the system 100 depicted in FIG. 1 is performed using the scan pattern 202 shown in FIG. 5. A fluorescence spectrum, two broadband reflectance spectra, and a reference spectrum are obtained at each region 204. The two broadband reflectance spectra use light incident to the sample at two different angles. A scan preferably begins at the center region 208, which corresponds to a pixel in a 500×480 pixel video image of the tissue sample at location 250, 240. As discussed in more detail below, a sequence of video images of the tissue sample may be taken during a scan of the 499 regions shown in FIG. 5 and may be used to detect and compensate for movement of the tissue sample during the scan. The real-time tracker component 106 of the system 100 shown in FIG. 1 performs this motion detection and compensation function. Preferably, the scanner assembly 180 of FIG. 3 includes controls for keeping track of the data obtained, detecting a stalled scan process, aborting the scan if the tissue is exposed to temperature or light outside of acceptable ranges, and/or monitoring and reporting errors detected by the spectral data acquisition component 104 of the system of FIG. 1.

[0216] FIG. 6 depicts front views of four exemplary arrangements 210, 212, 214, 216 of illumination sources about a probe head 192 according to various illustrative embodiments of the invention. The drawings are not to scale; they serve to illustrate exemplary relative arrangements of illumination sources about the perimeter of a probe head 192. Other arrangements include positioning collecting optics 200 around the perimeter of the probe head 192, about the illumination sources, or in any other suitable location relative to the illumination sources. The first arrangement 210 of FIG. 6 has one top illumination source 218 and one bottom illumination source 220, which are alternately cycled on and off as described above. The illumination sources are arranged about the collecting optics 200, which are located in the center of the probe head 192. Light from an illumination source is reflected from the tissue and captured by the collecting optics 200.

[0217] The second arrangement 212 of FIG. 6 is similar to the first arrangement 210, except that there are two illumination sources 222, 224 in the top half of the probe head 192 and two illumination sources 226, 228 in the bottom half of the probe head 192. In one embodiment, the two lights above the midline 230 are turned on and the two lights below the midline 230 are turned off while obtaining a first set of spectral data; then the lights above the midline 230 are turned off and the lights below the midline 230 are turned on while obtaining a second set of spectral data. In an alternate illustrative embodiment, only one of the four illumination sources are turned on at a time to obtain four sets of spectral data for a given region. Other illustrative embodiments include turning the illumination sources on and off in other patterns. Other alternative embodiments include using noncircular or otherwise differently shaped illumination sources, and/or using a different number of illumination sources.

[0218] The third arrangement 214 of FIG. 6 includes each illumination source 232, 234 positioned on either side of the probe head 192. The sources 232, 234 may be alternated in a manner analogous to those described for the first arrangement 210.

[0219] The fourth arrangement 216 of FIG. 6 is similar to the second arrangement 212, except that the illumination sources 236, 238 on the right side of the probe head 192 are turned off and on together, alternately with the illumination sources 240, 242 on the left side of the probe head 192. Thus, two sets of spectral data may be obtained for a given region, one set using the illumination sources 236, 238 on the right of the midline 244, and the other set using the illumination sources 240, 242 on the left of the midline 244.

[0220] FIG. 7 depicts exemplary illumination of a region 250 of a tissue sample 194 using light incident to the region 250 at two different angles 252, 254 according to an illustrative embodiment of the invention. FIG. 7 demonstrates that source light position may affect whether data is affected by glare. The probe head 192 of FIG. 7 is depicted in a cut-away view for illustrative purposes. In this illustrative embodiment, the top illumination source 188 and bottom illumination source 190 are turned on sequentially and illuminate the surface of a tissue sample 194 at equal and opposite angles relative to the collection axis 256. Arrows represent the light emitted 252 from the top illumination source 188, and the light specularly reflected 258 from the surface of the region 250 of the tissue sample 194. In preferred embodiments, it is desired to collect diffusely reflected light, as opposed to specularly reflected light 258 (glare). Since the specularly reflected light 258 from the top illumination source 188 does not enter the collecting optics 200 in the example illustrated in FIG. 7, a set of data obtained using the top illumination source 188 would not be affected by glare.

[0221] However, in the example illustrated in FIG. 7, the emitted light 254 from the bottom illumination source 190 reaches the surface of the region 250 of the tissue 194 and is specularly reflected into the collecting optics 200, shown by the arrow 260. Data obtained using the bottom illumination source 190 in the example pictured in FIG. 7 would be affected by glare. This data may not be useful, for example, in determining a characteristic or a condition of the region 250 of the tissue 194. In this example, it would be advantageous to instead use the set of data obtained using the top illumination source 188 since it is not affected by glare.

[0222] The position of the collection optics 200 may affect whether or not data is affected by glare. For example, light 252 with illumination intensity I_o(λ) strikes a tissue surface at a given region 250. A fraction of the initial illumination intensity, αI_o(λ), is specularly reflected from the surface 258, where a is a real number between 0 and 1. An acceptance cone 268 is the space through which light is diffusely reflected from the tissue 194 into the collecting optics 200, in this embodiment. Light may also be emitted or otherwise transmitted from the surface of the tissue. The diffusely reflected light is of interest, since spectral data obtained from diffusely reflected light can be used to determine the condition of the region of the sample. If there is no specular reflection within the acceptance cone 268, only diffusely reflected light is collected, and the collected signal corresponds to I_t(λ), where I_t(λ) is the intensity of light diffusely reflected from the region 250 on the surface of the tissue.

[0223] If the collection optics 200 are off-center, light incident to the tissue surface may specularly reflect within the acceptance cone 268. For example, light with illumination intensity I_o(λ) strikes the surface of the tissue. Light with a fraction of the initial illumination intensity, αI_o(λ), from a given source is specularly reflected from the surface 266, where α is a real number between 0 and 1. Where there is specular reflection of light within the acceptance cone 268, both diffusely reflected light and specularly reflected light reach the collecting optics 200. Thus, the collected signal corresponds to an intensity represented by the sum I_t(λ)+αI_o(λ). It may be difficult or impossible to separate the two components of the measured intensity, thus, the data may not be helpful in determining the condition of the region of the tissue sample due to the glare effect.

[0224] FIG. 8 is a diagram 284 depicting illumination of a region 250 of a cervical tissue sample 194 using a probe 142 and a vaginal speculum 286 according to an illustrative embodiment of the invention. Here, the illuminating light incident to the tissue sample 194, is depicted by the upper and lower intersecting cones 196, 198. In a preferred embodiment, the probe 142 operates without physically contacting the tissue being analyzed. In one embodiment, a disposable sheath 146 is used to cover the probe head 192, for example, in case of incidental contact of the probe head 192 with the patient's body. FIG. 9 is a schematic representation of an accessory device 290 that forms at least part of the disposable sheath 146 for a probe head 192 according to an illustrative embodiment of the invention. In one illustrative embodiment, the entire sheath 146, including the accessory device 290, if present, is disposed of after a single use on a patient. As shown in FIG. 8, in one illustrative embodiment, the disposable sheath 146 and/or the accessory device 290 have a unique identifier, such as a two-dimensional bar code 292. According to an illustrative feature, the accessory device 290 is configured to provide an optimal light path between the optical probe 142 and the target tissue 194. Optional optical elements in the accessory device 290 may be used to enhance the light transmitting and light receiving functions of the probe 142.

[0225] Although an illustrative embodiment of the invention is described herein with respect to analysis of vaginal tissue, other tissue types may be analyzed using these methods, including, for example, colorectal, gastroesophageal, urinary bladder, lung, skin tissue, and/or any tissue comprising epithelial cells.

[0226] FIG. 10 is a block diagram 300 featuring components of the tissue characterization system 100 of FIG. 1 that involve spectral data calibration and correction, according to an illustrative embodiment of the invention. The instrument 102 of FIG. 1 is calibrated at the factory, prior to field use, and may also be calibrated at regular intervals via routine preventive maintenance (PM). This is referred to as factory and/or preventive maintenance calibration 110. Additionally, calibration is performed immediately prior to each patient scan to account for temporal and/or intra-patient sources of variability. This is referred to as pre-patient calibration 116. The illustrative embodiment includes calibrating one or more elements of the instrument 102, such as the spectrometer and detector 168 depicted in FIG. 3.

[0227] Calibration includes performing tests to adjust individual instrument response and/or to provide corrections accounting for individual instrument variability and/or individual test (temporal) variability. During calibration procedures, data is obtained for the pre-processing of raw spectral data from a patient scan. The tissue classification system 100 of FIG. 1 includes determining corrections based on the factory and/or preventive maintenance calibration tests, indicated by block 112 in FIG. 10 and in FIG. 1. Where multiple sets of factory and/or preventive maintenance (PM) data exists, the most recent set of data is generally used to determine correction factors and to pre-process spectral data from a patient scan. Corrections are also determined based on pre-patient calibration tests, indicated by block 118 of FIG. 10. The correction factors are used, at least indirectly, in the pre-processing (114, FIG. 1) of fluorescence and reflectance spectral data obtained using a UV light source and two white light sources. Block 114 of FIG. 11 corresponds to the pre-processing of spectral data in the overall tissue classification system 100 of FIG. 1, and is further discussed herein.

[0228] Calibration accounts for sources of individual instrument variability and individual test variability in the preprocessing of raw spectral data from a patient scan. Sources of instrument and individual test variability include, for example, external light (light originating outside the instrument 102, such as room light) and internal stray light. Internal stray light is due at least in part to internal “cross talk,” or interaction between transmitted light and the collection optics 200. Calibration also accounts for the electronic background signal read by the instrument 102 when no light sources, internal or external, are in use. Additionally, calibration accounts for variations in the amount of light energy delivered to a tissue sample during a scan, spatial inhomogeneities of the illumination source(s), chromatic aberration due to the scanning optics, variation in the wavelength response of the collection optics 200, and/or the efficiency of the collection optics 200, for example, as well as other effects.

[0229] In the illustrative embodiment of FIG. 10, factory and preventive maintenance calibration tests are performed to determine correction factors 112 to apply to raw fluorescence and reflectance spectral data obtained during patient scans. The factory/preventive maintenance calibration tests 110 include a wavelength calibration test 302, a “null” target test 304, a fluorescent dye cuvette test 306, a tungsten source test 308, an “open air” target test 310, a customized target test 312, and a NIST standard target test 314.

[0230] The wavelength calibration test 302 uses mercury and argon spectra to convert a CCD pixel index to wavelengths (nm). A wavelength calibration and interpolation method using data from the mercury and argon calibration test 302 is described below.

[0231] The null target test 304 employs a target having about 0% diffuse reflectivity and is used along with other test results to account for internal stray light. Data from the factory/PM null target test 304 are used to determine the three correction factors shown in block 316 for fluorescence spectral measurements (F) obtained using a UV light source, and broadband reflectance measurements (BB1, BB2) obtained using each of two white light sources. In one embodiment, these three correction factors 316 are used in determining correction factors for other tests, including the factory/PM fluorescent dye cuvette test 306, the factory/PM open air target test 310, the factory/PM customized target test 312, and the factory/PM NIST standard target test 314. The open air target test 310, the customized target test 312, and the NIST standard target test 314 are used along with the null target test 304 to correct for internal stray light in spectral measurements obtained using a UV light source and one or more white light sources.

[0232] The open air target test 310 is performed without a target and in the absence of external light (all room lights turned off). The customized target test 312 employs a custom-designed target including a material of approximately 10% diffuse reflectivity and is performed in the absence of external light. The custom-designed target also contains phosphorescent and fluorescent plugs that are used during instrument focusing and target focus validation 122. In one embodiment, the custom-designed target is also used during pre-patient calibration testing (116, 330) to monitor the stability of fluorescence readings between preventive maintenance procedures and/or to align an ultraviolet (UV) light source 160—for example, a nitrogen laser or a frequency-tripled Nd:YAG laser. The NIST (U.S. National Institute of Standards and Technology) standard target test 314 employs a NIST-standard target comprising a material of approximately 60% diffuse reflectivity and is performed in the absence of external light. Correction factors determined from the “open air” target test 310, the custom target test 312, and the NIST-standard target test 314 are shown in blocks 322, 324, and 326 of FIG. 10, respectively. The correction factors are discussed in more detail below.

[0233] The fluorescent dye cuvette test 306 accounts for the efficiency of the collection optics 200 of a given unit. The illustrative embodiment uses data from the fluorescent dye cuvette test 306 to determine a scalar correction factor 318 for fluorescence measurements (F) obtained using a UV light source. The tungsten source test 308 uses a quartz-tungsten-halogen lamp to account for the wavelength response of the fluorescence collection optics 200, and data from this test are used to determine a correction factor 320 for fluorescence measurements (F) obtained using a UV light source.

[0234] In addition to factory and preventive maintenance calibration 110, pre-patient calibration 116 is performed immediately before each patient scan. The pre-patient calibration 116 includes performing a null target test 328 and a customized target test 330 before each patient scan. These tests are similar to the factory/PM null target test 304 and the factory/PM custom target test 312, except that they are each performed under exam room conditions immediately before a patient scan is conducted. The correction factors shown in blocks 332 and 334 of FIG. 10 are determined from the results of the pre-patient calibration tests. Here, correction factors (316, 322) from the factory/PM null target test 304 and the factory/PM open air test 310 are used along with pre-patient calibration data to determine the pre-patient correction factors 118, which are used, in turn, to pre-process raw spectral data from a patient scan, as shown, for example, in FIG. 11.

[0235] FIG. 11 is a block diagram 340 featuring the spectral data pre-processing component 114 of the tissue characterization system 100 of FIG. 1 according to an illustrative embodiment of the invention. In FIG. 1, “F” represents the fluorescence data obtained using the UV light source 160, “BB1” represents the broadband reflectance data obtained using the first 188 of the two white light sources 162 and “BB2” represents the broadband reflectance data obtained using the second 190 of the two white light sources 162. Blocks 342 and 344 indicate steps undertaken in pre-processing raw reflectance data obtained from the tissue using each of the two white light sources 188, 190, respectively. Block-346 indicates steps undertaken in pre-processing raw fluorescence data obtained from the tissue using the UV light source 160. These steps are discussed in more detail below.

[0236] The instrument 102 detailed in FIG. 3 features a scanner assembly 180 which includes a CCD (charge couple device) detector and spectrograph for collecting fluorescence and reflectance spectra from tissue samples. Because a CCD detector is used, the system employs a calibration procedure to convert a pixel index into wavelength units. Referring to FIG. 10, the pixel-to-wavelength calibration 302 is performed as part of factory and/or preventive maintenance calibration procedures 110.

[0237] In the illustrative embodiment, the tissue classification system 100 uses spectral data obtained at wavelengths within a range from about 360 nm to about 720 nm. Thus, the pixel-to-wavelength calibration procedure 302 uses source light that produces peaks near and/or within the 360 nm to 720 nm range. A mercury lamp produces distinct, usable peaks between about 365 nm and about 578 nm, and an argon lamp produces distinct, usable peaks between about 697 nm and about 740 nm. Thus, the illustrative embodiment uses mercury and argon emission spectra to convert a pixel index from a CCD detector into units of wavelength (nm).

[0238] First, a low-pressure pen-lamp style mercury lamp is used as source light, and intensity is plotted as a function of pixel index. The pixel indices of the five largest peaks are correlated to ideal, standard Hg peak positions in units of nanometers. Second, a pen-lamp style argon lamp is used as source light and intensity is plotted as a function of pixel index. The two largest peaks are correlated to ideal, standard Ar peak positions in units of nanometers.

[0239] The seven total peaks provide a set of representative peaks well-distributed within a range from about 365 nm to about 738 nm—comparable to the range from about 360 nm to about 720 nm that is used for data analysis in the tissue classification system 100. The calibration procedure in block 302 of FIG. 10 includes retrieving the following spectra: a spectrum using a mercury lamp as light source, a mercury background spectrum (a spectrum obtained with the mercury source light turned off), a spectrum using an argon lamp as light source, and an argon background spectrum. The respective Hg and Ar background spectra are subtracted from the Hg and Ar spectra, producing the background-corrected Hg and Ar spectra. The spectra are essentially noise-free and require no smoothing. Each of the seven pixel values corresponding to the seven peaks above are determined by finding the centroid of the curve of each peak over a +/−5 pixel range of the maximum as shown in Equation 1:

[0240] $\begin{array}{cc}\mathrm{centroid}={\int }_{p_{\max }^{-5}}^{p_{\max }^{+5}}p{I}_{p}dp/{\int }_{p_{\max }^{-5}}^{p_{\max }^{+5}}{I}_{p}dp,& \left(1\right)\end{array}$
where p is pixel value, I_p is the intensity at pixel p, and p_max is the pixel value corresponding to each peak maximum. From the p_max determinations, a polynomial function correlating pixel value to wavelength value is determined by performing a least-squares fit of the peak data. In one embodiment, the polynomial function is of fourth order. In alternative embodiments, the polynomial is of first order, second order, third order, fifth order, or higher order.

[0241] Alternatively to finding p_max by determining the centroid as discussed above, in another illustrative embodiment the pixel-to-wavelength calibration procedure 302 includes fitting a second order polynomial to the signal intensity versus pixel index data for each of the seven peaks around the maximum +/−3 pixels (range including 7 pixels); taking the derivative of the second order polynomial; and finding the y-intercept to determine each p_max.

[0242] The resulting polynomial function correlating pixel value to wavelength value is validated, for example, by specifying that the maximum argon peak be located within a given pixel range, such as [300:340] and/or that the intensity count at the peak be within a reasonable range, such as between 3000 and 32,000 counts. Additionally, the maximum mercury peak is validated to be between pixel 150 and 225 and to produce an intensity count between 3000 and 32,000 counts. Next, the maximum difference between any peak wavelength predicted by the polynomial function and its corresponding ideal (reference) peak is required to be within about 1.0 nm. Alternatively, other validation criteria may be set.

[0243] Additional validation procedures may be performed to compare calibration results obtained for different units, as well as stability of calibration results over time. In one illustrative embodiment, the pixel-to-wavelength calibration 302 and/or validation is performed as part of routine preventive maintenance procedures.

[0244] Since fluorescence and reflectance spectral data that are used as reference data in the classification system 100 may be obtained at multiple clinical sites with different individual instruments, the illustrative system 100 standardizes spectral data in step 302 of FIG. 10 by determining and using values of spectral intensity only at designated values of wavelength. Spectral intensity values are standardized by interpolating pixel-based intensities such that they correspond to wavelengths that are spaced every 1 nm between about 360 nm and about 720 nm. This may be done by linear interpolation of the pixel-based fluorescence and/or reflectance values. Other illustrative embodiments use, for example, a cubic spline interpolation procedure instead of linear interpolation.

[0245] In some illustrative embodiments, spectral data acquisition during patient scans and during the calibration procedures of FIG. 10 includes the use of a CCD array as part of the scanner assembly 180 depicted in FIG. 3. The CCD array may contain any number of pixels corresponding to data obtained at a given time and at a given interrogation point. In one embodiment, the CCD array contains about 532 pixels, including unused leading pixels from index 0 to 9, relevant data from index 10 to 400, a power monitor region from index 401 to 521, and unused trailing pixels from index 522 to 531. One embodiment includes “power correcting” or “power monitor correcting” by scaling raw reflectance and/or fluorescence intensity measurements received from a region of a tissue sample with a measure of the intensity of light transmitted to the region of the tissue sample. In order to provide the scaling factor, the instrument 102 directs a portion of a light beam onto the CCD array, for example, at pixel indices 401 to 521, and integrates intensity readings over this portion of the array.

[0246] In one preferred embodiment, both factory/PM 110 and pre-patient 116 calibration accounts for chromatic, spatial, and temporal variability caused by system interference due to external stray light, internal stray light, and electronic background signals. External stray light originates from sources external to the instrument 102, for example, examination room lights and/or a colposcope light. The occurrence and intensity of the effect of external stray light on spectral data is variable and depends on patient parameters and the operator's use of the instrument 102. For example, as shown in FIG. 8, the farther the probe head 192 rests from the speculum 286 in the examination of cervical tissue, the greater the opportunity for room light to be present on the cervix. The configuration and location of a disposable component 146 on the probe head 192 also affects external stray light that reaches a tissue sample. Additionally, if the operator forgets to turn off the colposcope light before taking a spectral scan, there is a chance that light will be incident on the cervix and affect spectral data obtained.

[0247] Electronic background signals are signals read from the CCD array when no light sources, internal or external, are in use. According to the illustrative embodiment, for all components of the tissue characterization system 100 that involve obtaining and/or using spectral data, including components 110, 116, 104, and 114 of FIG. 1, both external stray light and electronic background signals are taken into account by means of a background reading. For each interrogation point in a spectral scan in which one or more internal light sources are used, a background reading is obtained in which all internal light sources (for example, the Xenon lamps and the UV laser) are turned off. According to one feature, the background reading immediately precedes the fluorescence and broadband reflectance measurements at each scan location, and the system 100 corrects for external stray light and electronic background by subtracting the background reading from the corresponding spectral reading at a given interrogation point. In FIG. 10, each calibration test—including 304, 306, 308, 310, 312, 314, 328, and 330—includes obtaining a background reading at each interrogation point and subtracting it from the test reading to account for external stray light and electronic background signals. Also, background subtraction is a step in the spectral data preprocessing 114 methods in FIG. 11, for the pre-processing of raw BB1 and BB2 reflectance data 342, 344 as well as the pre-processing of raw fluorescence data 346.

[0248] Equation 2 shows the background correction for a generic spectral measurement from a tissue sample, S_{tissue+ISL+ESL+EB}(i,λ):
S_tissue+ISL(i,λ)=S_{tissu+ISL+ESL+EB}(i,λ)−Bk_EB+ESL(i,λ)  (2)
where i corresponds to a scan location; λ is wavelength or its pixel index equivalent; and subscripts denote influences on the spectral measurement—where “tissue” represents the tissue sample, “ISL” represents internal stray light (internal to the instrument 102), “ESL” represents external stray light, and “EB” represents electronic background. S_{tissue+ISL+ESL+EB}(i,λ) is a two-dimensional array (which may be power-monitor corrected) of spectral data obtained from the tissue at each interrogation point (region) i as a function of wavelength λ; and Bk_EB+ESL(i,λ) is a two-dimensional array representing values of the corresponding background spectral readings at each point i as a function of wavelength λ. S_tissue+ISL(i,λ) is the background-subtracted spectral array that is thereby corrected for effects of electronic background (EB) and external stray light (ESL) on the spectral data from the tissue sample. The electronic background reading is subtracted on a wavelength-by-wavelength, location-by-location basis. Subtracting the background reading generally does not correct for internal stray light (ISL), as denoted in the subscript of S_tissue+ISl(i,λ).

[0249] Internal stray light includes internal cross talk and interaction between the transmitted light within the system and the collection optics. For fluorescence measurements, a primary source of internal stray light is low-level fluorescence of optics internal to the probe 142 and the disposable component 146. For reflectance measurements, a primary source of internal stray light is light reflected off of the disposable 146 and surfaces in the probe 142 that is collected through the collection optics 200. The positioning of the disposable 146 can contribute to the effect of internal stray light on reflectance measurements. For example, the internal stray light effect may vary over interrogation points of a tissue sample scan in a non-random, identifiable pattern due to the position of the disposable during the test.

[0250] According to the illustrative embodiment of FIG. 10, the factory/PM null target test 304, the factory/PM open air target test 306, the factory/PM custom target test 312, the factory/PM NIST target test 314, the pre-patient null target test 328, and the pre-patient custom target test 330 provide correction factors to account for internal stray light effects on fluorescence and reflectance spectral measurements. In an alternative illustrative embodiment, a subset of these tests is used to account for internal stray light effects.

[0251] The null target test 304, 328, performed in factory/preventive maintenance 110, and pre-patient 116 calibration procedures, uses a target that has a theoretical diffuse reflectance of 0%, although the actual value may be higher. Since, at least theoretically, no light is reflected by the target, the contribution of internal stray light can be measured for a given internal light source by obtaining a spectrum from a region or series of regions of the null target with the internal light source turned on, obtaining a background spectrum from the null target with the internal light source turned off, and background-subtracting to remove any effect of electronic background signal or external stray light. The background-subtracted reading is then a measure of internal stray light. The pre-patient null target test 328 takes into account spatially-dependent internal stray light artifacts induced by the position of a disposable 146, as well as temporal variability induced, for example, by the aging of the instrument and/or dust accumulation. In one embodiment, the factory/PM null target test 304 is used in calculating correction factors from other factory and/or preventive maintenance calibration procedures. The null target tests 304, 328 are not perfect, and improved measurements of the effect of internal stray light on spectral data can be achieved by performing additional tests.

[0252] The open air target test 310 is part of the factory preventive maintenance (PM) calibration procedure 110 of FIG. 10 and provides a complement to the null target tests 304, 328. The open air target test 310 obtains data in the absence of a target with the internal light sources turned on and all light sources external to the device turned off, for example, in a darkroom. The null target test 304, by contrast, does not have to be performed in a darkroom since it uses a target in place in the calibration port, thereby sealing the instrument such that measurements of light from the target are not affected by external light. Although a disposable 146 is in place during open air test measurements, the factory/PM open air target test 310 does not account for any differences due to different disposables used in each patient run. The open air measurements are important in some embodiments, however, since they are performed under more controlled conditions than pre-patient calibration tests 116, for example, the open air tests may be performed in a darkroom. Also, the factory/PM calibration 110 measurements account for differences between individual instruments 102, as well as the effects of machine aging—both important factors since reference data obtained by any number of individual instruments 102 are standardized for use in a tissue classification algorithm, such as the one depicted in block 132 of FIG. 1.

[0253] FIGS. 12, 13, 14, and 15 show graphs demonstrating mean background-subtracted, power-monitor-corrected intensity readings from a factory open air target test 310 and a null target test 304 using a BB1 reflectance white light source and a UV light source (laser). FIG. 12 shows a graph 364 of mean intensity 366 from an open air target test over a set of regions as a function of wavelength 368 using a BB1 reflectance white light source 188—the “top” source 188 as depicted in FIGS. 4, 7, and 8. FIG. 13 shows a graph 372 of mean intensity 366 from a null target test over the set of regions as a function of wavelength 368 using the same BB1 light source. Curves 370 and 374 are comparable but there are some differences.

[0254] FIG. 14 shows a graph 376 of mean intensity 378 from an open air target test over a set of regions as a function of wavelength 380 using a UV light source, while FIG. 15 shows a graph 384 of mean intensity 378 from a null target test over the set of regions as a function of wavelength 380 using the UV light source. Again, curves 382 and 386 are comparable, but there are some differences between them. Differences between the open air test intensity and null target test intensity are generally less than 0.1% for reflectance data and under 1 count/μJ for fluorescence data.

[0255] Accounting for internal stray light is more complicated for reflectance measurements than for fluorescence measurements due to an increased spatial dependence. The open air target test measurement, in particular, has a spatial profile that is dependent on the position of the disposable.

[0256] FIG. 16 shows a representation 390 of regions of an exemplary scan performed in a factory open air target test. The representation 390, shows that broadband intensity readings can vary in a non-random, spatially-dependent manner. Other exemplary scans performed in factory open air target tests show a more randomized, less spatially-dependent variation of intensity readings than the scan shown in FIG. 16.

[0257] According to the illustrative embodiment, the system 100 of FIG. 1 accounts for internal stray light by using a combination of the results of one or more open air target tests 310 with one or more null target tests 304, 328. In an alternative embodiment, open air target test data is not used at all to correct for internal stray light, pre-patient null target test data being used instead.

[0258] Where open air and null target test results are combined, it is helpful to avoid compounding noise effects from the tests. FIG. 17 shows a graph 402 depicting as a function of wavelength 406 the ratio 404 of the background-corrected, power-monitor-corrected reflectance spectral intensity at a given region using an open air target to the reflectance spectral intensity at the region using a null target according to an illustrative embodiment of the invention. The raw data 407 is shown in FIG. 17 fit with a second-order polynomial 412, and fit with a third-order polynomial without filtering 410, and with filtering 408. As seen by the differences between curve 407 and curves 408, 410, and 412, where a ratio of open air target data and null target data are used to correct for internal stray light in reflectance measurements, a curve fit of the raw data reduces the effect of noise. This is shown in more detail herein with respect to the calculation of pre-patient corrections 118 in FIG. 10. Also evident in FIG. 17 is that the open air measurement generally differs from the null target measurement, since the ratio 404 is not equal to 1, and since the ratio 404 has a distinct wavelength dependence.

[0259] FIG. 18 shows a graph 414 depicting as a function of wavelength 418 the ratio 416 of fluorescence spectral intensity using an open air target to the fluorescence spectral intensity using a null target according to an illustrative embodiment of the invention. The raw data 420 does not display a clear wavelength dependence, except that noise increases at higher wavelengths. A mean 422 based on the ratio data 420 over a range of wavelengths is plotted in FIG. 18. Where a ratio of open air target to null target data is used to correct for internal stray light in fluorescence measurements, using a mean value calculated from raw data over a stable range of wavelength reduces noise and does not ignore any clear wavelength dependence.

[0260] FIG. 10 shows correction factors corresponding to open air 310 and null target 304, 328 calibration tests in one embodiment that compensates spectral measurements for internal stray light effects. There are three types of spectral measurements in FIG. 10—fluorescence (F) measurements and two reflectance measurements (BB1, BB2) corresponding to data obtained using a UV light source and two different white light sources, respectively. The corrections in blocks 316, 322, and 332 come from the results of the factory/PM null target test 304, the factory/PM open air target test 310, and the pre-patient null target test 328, respectively, and these correction factors are applied in spectral data pre-processing (FIG. 11) to compensate for the effects of internal stray light. These correction factors are described below in terms of this embodiment.

[0261] Block 316 in FIG. 10 contains correction factors computed from the results of the null target test 304, performed during factory and/or preventive maintenance (PM) calibration. The null target test includes obtaining a one-dimensional array of mean values of spectral data from each channel—F, BB1, and BB2—corresponding to the three different light sources, as shown in Equations 3, 4, and 5:
FCNULLFL=<I_nt,F(i,λ,t_o)>_i  (3)
FCNULLBB1=<I_nt,BB1(i,λ,t_o)>_i  (4)
FCNULLBB2=<I_nt,BB2(i,λ,t_o)>_i  (5)
where I_nt refers to a background-subtracted, power-monitor-corrected two-dimensional array of spectral intensity values; subscript F refers to intensity data obtained using the fluorescence UV light source; subscripts BB1 and BB2 refer to intensity data obtained using the reflectance BB1 and BB2 white light sources, respectively; i refers to interrogation point “i” on the calibration target; λ refers to a wavelength at which an intensity measurement corresponds or its approximate pixel index equivalent; t_o refers to the fact the measurement is obtained from a factory or preventive maintenance test, the “time” the measurement is made; and < >_i represents a one-dimensional array (spectrum) of mean values computed on a pixel-by-pixel basis for each interrogation point, i. In this embodiment, a one-dimensional array (spectrum) of fluorescence values corresponding to wavelengths from λ=370 nm to λ=720 nm is obtained at each of 499 interrogation points, i. An exemplary scan pattern 202 of 499 interrogation points appears in FIG. 5. In the illustrative embodiment, data from an additional interrogation point is obtained from a region outside the target 206. Each of the reflectance intensity spectra is obtained over the same wavelength range as the fluorescence intensity spectra, but the BB1 data is obtained at each of 250 interrogation points over the bottom half of the target and the BB2 data is obtained at each of 249 interrogation points over the top half of the target. This avoids a shadowing effect due to the angle at which the light from each source strikes the target during the null target test 304. Values of the most recent factory or preventive maintenance calibration test, including the factory/PM null target test 304, are used in spectral data pre-processing (FIG. 11) for each patient scan.

[0262] The pre-patient null target test, shown in block 328 of FIG. 10, is similar to the factory/PM null target test 304, except that it is performed just prior to each patient test scan. Each pre-patient null target test 328 produces three arrays of spectral data as shown below:
I_nt,F(i,λ,t′)  (6)
I_nt,BB1(i,λ,t′)  (7)
I_nt,BB2(i,λ,t′)  (8)
where t′ refers to the fact the measurements are obtained just prior to the test patient scan, as opposed to during factory/PM testing (t_o).

[0263] Block 332 in FIG. 10 contains correction factors from the open air target test 310, preformed during factory and/or preventive maintenance (PM) calibration 110. The open air target test is performed with the disposable in place, in the absence of a target, with the internal light sources turned on, and with all light sources external to the device turned off. The open air target test 310 includes obtaining an array of spectral data values from each of the three channels—F, BB1, and BB2—as shown below:
I_oa,F(i,λ,t_o)  (9)
I_oa,BB1(i,λ,t_o)  (10)
I_oa,BB2(i,λ,t_o)  (11)

[0264] In each of items 9, 10, and 11 above, I_oa refers to a background-subtracted, power-monitor-corrected array of spectral intensity values; i runs from interrogation points 1 to 499; and λ runs from 370 nm to 720 nm (or the approximate pixel index equivalent).

[0265] According to the illustrative embodiment, correction for internal stray light makes use of both null target test results and open air target test results. Correction factors in block 322 of FIG. 10 use results from the factory/PM null target test 304 and factory/PM open air target test 310. The correction factors in block 322 are computed as follows:
sFCOFL=[<I_oa,F(i,λ,t_o)>_i/<I_nt,F(i,λ,t_o)>_i]_{mean, λ=375 nm to 470 nm}  (12)
FCOBB1=fitted forn of <I_oa,BB1(i,λ,t_o)>_i/<I_nt,BB1(i,λ,t_o)>_i  (13)
FCOBB2=fitted form of <I_oa,BB2(i,λ,t_o)>_i/<I_nt,BB2(i,λ,t_o)>_i  (14)
where < >_i represents a spectrum (1-dimensional array) of mean values computed on a pixel-by-pixel basis for each interrogation point i, and where < >_i/< >_i represents a spectrum (1-dimensional array) of quotients (ratios of means) computed on a pixel-by-pixel basis for each interrogation point i. The correction factor sFCOFL in Equation 12 is a scalar quantity representing the mean value of the 1-dimensional array in brackets [ ] across pixel indices corresponding to the wavelength range of about 375 nm to about 470 nm.

[0266] FIG. 18 shows an example value of sFCOFL 422 evaluated using a set of mean open air spectral data and mean null target spectral data. Large oscillations are damped by using the mean in Equation 12. Other wavelength ranges can be chosen instead of the wavelength range of about 375 nm to about 470 nm.

[0267] The one-dimensional arrays, FCOBB1 and FCOBB2, are obtained by curve-fitting the spectra of quotients in Equations 13 and 14 with second-order polynomials and determining values of the curve fit corresponding to each pixel. FIG. 17 shows an example curve fit for FCOBB1 (412). Unlike the fluorescence measurements, there is wavelength dependence of this ratio, and a curve fit is used to properly reflect this wavelength dependence without introducing excessive noise in following computations.

[0268] Block 332 in FIG. 10 contains correction factors using results from the pre-patient null target test 328, as well as the most recent factory/PM null target test 304 and open air target test 310. The correction factors in block 332 are computed as follows:
SLFL=sFCOFL·<I_nt,F(i,λ,t′)>_i  (15)
SLBB1=FCOBB1·<I_nt,BB1(i,λ,t′)>_i  (16)
SLBB2=FCOBB2·<I_nt,BB2(i,λ,t′)>_i  (17)
where Equation 15 represents multiplying each value in the fluorescence mean pre-patient null target spectrum by the scalar quantity sFCOFL from Equation 12; Equation 16 represents multiplying corresponding elements of the mean pre-patient null target BB1 spectrum and the one-dimensional array FCOBB1 from Equation 13; and Equation 17 represents multiplying corresponding elements of the mean pre-patient null target BB2 spectrum and the one-dimensional array FCOBB2 from Equation 14. Each of SLFL, SLBB1, and SLBB2 is a one-dimensional array.

[0269] The correction factors in block 332 of FIG. 10 represent the contribution due to internal stray light (ISL) for a given set of spectral data obtained from a given patient scan. Combining equations above:
SLFL=[<I_oa,F(i,λ,t_o)>_i/<I_nt,F(i,λ,t_o)>_i]_{mean, λ=375 nm to 470 nm}·<I_nt,F(i,λ,t′)>_i  (18)
SLBB1=[<I_oa,BB1(i,λ,t_o)>_i/<I_nt,BB1(i,λ,t_o)>_i]_fitted·<I_nt,BB1(i,λ,t′)>_i  (19)
SLBB2=[<I_oa,BB2(i,λ,t_o)>_i/<I_nt,BB2(i,λ,t_o)>_i]_fitted·<I_nt,BB2(i,λ,t′)>_i  (20)

[0270] Alternative internal stray light correction factors are possible. For example, in one alternative embodiment, the scalar quantity in Equation 18 is replaced with the value 1.0. In one alternative embodiment, the first term on the right side of either or both of Equation 19 and Equation 20 is replaced with a scalar quantity, for example, a mean value or the value 1.0.

[0271] Spectral data preprocessing 114 as detailed in FIG. 11 includes compensating for internal stray light effects as measured by SLFL, SLBB1 and SLBB2. In one embodiment, a patient scan includes the acquisition at each interrogation point in a scan pattern (for example, the 499-point scan pattern 202 shown in FIG. 5) of a set of raw fluorescence intensity data using the UV light source 160, a first set of raw broadband reflectance intensity data using a first white light source (162, 188), a second set of raw broadband reflectance intensity data using a second white light source (162, 192), and a set of raw background intensity data using no internal light source, where each set of raw data spans a CCD pixel index corresponding to a wavelength range between about 370 nm and 720 nm. In another embodiment, the wavelength range is from about 370 nm to about 700 nm. In another embodiment, the wavelength range is from about 300 nm to about 900 nm. Other embodiments include the use of different wavelength ranges.

[0272] The raw background intensity data set is represented as the two-dimensional array Bkgnd[ ] in FIG. 11. Spectral data processing 114 includes subtracting the background array, Bkgnd[ ], from each of the raw BB1, BB2, and F arrays on a pixel-by-pixel and location-by-location basis. This accounts at least for electronic background and external stray light effects, and is shown as item #1 in each of blocks 342, 344, and 346 in FIG. 11.

[0273] Also, each CCD array containing spectral data includes a portion for monitoring the power output by the light source used to obtain the spectral data. In one embodiment, the intensity values in this portion of each array are added or integrated to provide a one-dimensional array of scalar values, sPowerMonitor[ ], shown in FIG. 11. Spectral data pre-processing 114 further includes dividing each element of the background-subtracted arrays at a given interrogation point by the power monitor scalar correction factor in sPowerMonitor[ ] corresponding to the given interrogation point. This allows the expression of spectral data at a given wavelength as a ratio of received light intensity to transmitted light intensity.

[0274] Spectral data pre-processing 114 further includes subtracting each of the stray light background arrays—SLBB1, SLBB2, and SLFL—from its corresponding background-corrected, power-monitor-corrected spectral data array—BB1, BB2, and F—on a pixel-by-pixel, location-by-location basis. This accounts for chromatic, temporal, and spatial variability effects of internal stray light on the spectral data.

[0275] The remaining steps in blocks 342 and 344 of the spectral data pre-processing block diagram 340 of FIG. 11 include further factory, preventive maintenance (PM) and/or pre-patient calibration of reflectance (BB1, BB2) measurements using one or more targets of known, non-zero diffuse reflectance. In the embodiment shown in FIG. 10, this calibration uses results from the factory/PM custom target test 312, the factory/PM NIST-standard target test 314, and the pre-patient custom target test 330. These calibration tests provide correction factors as shown in blocks 324, 326, and 334 of FIG. 10, that account for chromatic, temporal, and spatial sources of variation in broadband reflectance spectral measurements. These sources of variation include temporal fluctuations in the illumination source, spatial inhomogeneities in the illumination source, and chromatic aberration due to the scanning optics. The broadband reflectance calibration tests (312, 314, 330) also account for system artifacts attributable to both transmitted and received light, since these artifacts exist in both test reflectance measurements and known reference measurements.

[0276] According to the illustrative embodiment, reflectance, R, computed from a set of regions of a test sample (a test scan) is expressed as in Equation 21:
R=[Measurement/Reference Target]·Reflectivity of Reference Target  (21)
where R, Measurement, and Reference Target refer to two-dimensional (wavelength, position) arrays of background-corrected, power-corrected and/or intemal-stray-light-corrected reflectance data; Measurement contains data obtained from the test sample; Reference Target contains data obtained from the reference target; Reflectivity of Reference Target is a known scalar value; and division of the arrays is performed in a pixel-by-pixel, location-by-location manner.

[0277] The factory/PM NIST target test 314 uses a 60%, NIST-traceable, spectrally flat diffuse reflectance target in the focal plane, aligned in the instrument 102 represented in FIG. 3. The NIST target test 314 includes performing four scans, each of which proceed with the target at different rotational orientations, perpendicular to the optical axis of the system. For example, the target is rotated 90° from one scan to the next. The results of the four scans are averaged on a location-by-location, pixel-by-pixel basis to remove spatially-dependent target artifacts (speckling) and to reduce system noise. The goal is to create a spectrally clean (low noise) and spatially-flat data set for application to patient scan data. In one embodiment, the NIST target test 314 is performed only once, prior to instrument 102 use in the field (factory test), and thus, ideally, is temporally invariant.

[0278] The custom target tests 312, 330 use a custom-made target for both factory and/or preventive maintenance calibration, as well as pre-patient calibration of reflectance data. The custom target is a 10% diffuse reflective target with phosphorescent and/or fluorescent portions used, for example, to align the ultraviolet (UV) light source and/or to monitor the stability of fluorescence readings between-preventive maintenance procedures. FIG. 19 is a photograph of the custom target 426 according to an illustrative embodiment. In FIG. 19, the target 426 includes a portion 428 that is about 10% diffuse reflective material, with four phosphorescent plugs 430, 432, 434, 436 equally-spaced at the periphery and a single fluorescent plug 438 at the center. As a result of the plugs, not all scan locations in the scan pattern 202 of FIG. 5, as applied to the custom target test 426, accurately measure the 10% reflective portion. Thus, a mask provides a means of filtering out the plug-influenced portions of the custom target 426 during a custom target calibration scan 312, 330.

[0279] FIG. 20 is a representation of such a mask 444 for the custom target reflectance calibration tests 312, 330. Area 445 in FIG. 20 corresponds to regions of the custom target 426 of FIG. 19 that are not affected by the plugs 430, 432, 434, 436, and which, therefore, are usable in the custom target reflectance calibration tests 312, 330. Areas 446, 448, 450, 452, and 454 of FIG. 20 correspond to regions of the custom target 426 that are affected by the plugs, and which are masked out in the custom target calibration scan results.

[0280] In the illustrative embodiment, the factory/PM NIST target test 314 provides reflectance calibration data for a measured signal from a test sample (patient scan), and the test sample signal is processed according to Equation 22:
R(i,λ,t′)=[I_m(i,λ,t′)/I_fc(i,λ,t_o)]·0.6  (22)

[0281] Where R, I_m, and I_fc are two-dimensional arrays of background-corrected, power-corrected reflectance data; R contains reflectance intensity data from the test sample adjusted according to the reflectance calibration data; I_m contains reflectance intensity data from the sample, I_fc contains reflectance intensity data from the factory/PM NIST-standard target test 314, and 0.6 is the known reflectivity of the NIST-standard target. Equation 22 presumes the spectral response of the illumination source is temporally invariant such that the factory calibration data from a given unit does not change with time, as shown in Equation 23 below:
I_fc(t′)=I_fc(t_o)  (23)
However, the spectral lamp function of a xenon flash lamp, as used in the illustrative embodiment as the white light source 162 in the instrument 102 of FIG. 3, is not invariant over time.

[0282] The illustrative reflectance data spectral preprocessing 114 accounts for temporal variance by obtaining pre-patient custom target test (330) reflectance calibration data and using the data to adjust data from a test sample, I_m, to produce adjusted reflectance R, as follows:
R(i,λ,t′)=[I_m(i,λ,t′)/(I_cp(i,λ,t′)>_i]·0.1  (24)
where masked, mean reflectance intensity data from the pre-patient custom target test 330 with 10% diffuse reflectivity, (I_cp(i,λ,t′)>, replaces I_fc(i,λ,t′) in Equation 22. Since the pre-patient custom target test data is updated before every patient exam, the temporal variance effect is diminished or eliminated. In other illustrative embodiments, various other reference targets may be used in place of the custom target 426 shown in FIG. 19.

[0283] The system 100 also accounts for spatial variability in the target reference tests of FIG. 10 in pre-processing reflectance spectral data. Illustratively, spatial variability in reflectance calibration target intensity is dependent on wavelength, suggesting chromatic aberrations due to wavelength-dependence of transmission and/or collection optic efficiency.

[0284] The illustrative reflectance data spectral preprocessing 114 accounts for these chromatic and spatial variability effects by obtaining reflectance calibration data and using the data to adjust data from a test sample, I_m, to produce adjusted reflectance R, as follows:
R(i,λ,t′)=[I_m(i,λ,t′)/I_cp(i,λ,t′)>_i]·[<I_fc(i,λ,t_o)>_i/I_fc(i,λ,t_o)]·0.1  (25)
Equation 25 accounts for variations of the intensity response of the lamp by applying the pre-patient custom-target measurements—which are less dependent on differences caused by the disposable—in correcting patient test sample measurements. Equation 25 also accounts for the spatial response of the illumination source by applying the factory NIST-target measurements in correcting patient test sample measurements.

[0285] In an alternative illustrative embodiment, the NIST-target test 314 is performed as part of pre-patient calibration 116 to produce calibration data, I_fc(i,λ,t′), and Equation 22 is used in processing test reflectance data, where the quantity I_fc(i,λ,t′) replaces the quantity I_fc(i,λ,t_o) in Equation 22. According to this illustrative embodiment, the test data pre-processing procedure 114 includes both factory/PM calibration 110 results and pre-patient calibration 116 results in order to maintain a more consistent basis for the accumulation and use of reference data from various individual units obtained at various times from various patients in a tissue characterization system. Thus, this illustrative embodiment uses Equation 26 below to adjust data from a test sample, I_m, to produce adjusted reflectance R, as follows:
R(i,λ,t′)=[I_m(i,λ,t′)/<I_fc(i,λ,t′)>_i]·[(<I_fc(i,λ,t_o)>_i/I_fc(i,λ,t_o)]·0.6  (26)
where the NIST-standard target test 314 is performed both as a factory/PM test 110 (t_o) and as a pre-patient test 116 (t′).

[0286] According to the illustrative embodiment, it is preferable to combine calibration standards with more than one target, each having a different diffuse reflectance, since calibration is not then tied to a single reference value. Here, processing using Equation 25 is preferable to Equation 26. Also, processing via Equation 25 may allow for an easier pre-patient procedure, since the custom target combines functions for both fluorescence and reflectance system set-up, avoiding the need for an additional target test procedure.

[0287] Values of the custom target reflectance in a given individual instrument 102 vary over time and as a function of wavelength. For example, FIG. 21 shows a graph 458 depicting as a function of wavelength 462 a measure of the mean reflectivity 460, R_cp, of the 10% diffuse target 426 of FIG. 19 over the non-masked regions 445 shown in FIG. 20, obtained using the same instrument on two different days. R_cp is calculated as shown in Equation 27:
R_cp(λ)=[<I_cp(i,λ,t_o)>_i/<I_fc(i,λ,t_o)>_i]·R_fc  (27)
where R_fc=0.6, the diffuse reflectance of the NIST-traceable standard target. Values of R_cp vary as a function of wavelength 462, as seen in each of curves 464 and 466 of FIG. 21. Also, there is a shift from curve 464 to curve 466, each obtained on a different day. Similarly, values of R_cp vary among different instrument units. Curves 464 and 466 show that R_cp varies with wavelength and varies from 0.1; thus, assuming R_cp=0.1 as in Equation 25 may introduce inaccuracy.

[0288] Equation 25 can be modified to account for this temporal and wavelength dependence, as shown in Equation 28:
R(i,λ,t′)=[I_m(i,λ,t′)/<I_cp(i,λ,t′)>_i]·[<I_fc(i,λ,t_o)>_i/I_fc(i,λ,t_o)]·R_cp,fitted  (28)
where R_cp,fitted is an array of values of a second-order polynomial curve fit of R_cp shown in Equation 27. The polynomial curve fit reduces the noise in the R_cp array. Other curve fits may be used alternatively. For example, FIG. 22A shows a graph 490 depicting, for seven individual instruments, curves 496, 498, 500, 502, 504, 506, 508 of sample reflectance intensity using the BB1 white light source 188 as depicted in FIGS. 4, 7 and 8 graphed as functions of wavelength 494. Each of the seven curves represents a mean of reflectance intensity at each wavelength, calculated using Equation 25 for regions confirmed as metaplasia by impression. FIG. 22B shows a graph 509 depicting corresponding curves 510, 512, 514, 516, 518, 520, 522 of test sample reflectance intensity calculated using Equation 28, where R_cp varies with time and wavelength. The variability between individual instrument units decreases when using measured values for R_cp as in Equation 28 rather than as a constant value. The variability between reflectance spectra obtained from samples having a common tissue-class/state-of-health classification, but using different instrument units decreases when using measured values for R_cp as in Equation 28 rather than a constant value as in Equation 25.

[0289] In an alternative embodiment, processing of reflectance data includes applying Equation 28 without first fitting R_cp values to a quadratic polynomial. Thus, processing is performed in accordance with Equation 29 to adjust data from a test sample, I_m, to produce adjusted reflectance R, as follows:
R(i,λ,t′)=[I_m(i,λ,t′)/<I_cp(i,λ,t′)>_i]·[<I_fc(i,λ,t_o)>_i/I_fc(i,λ,t_o)]·R_cp  (29)

[0290] Applying Equation 29, however, introduces an inconsistency in the reflectance spectra at about 490 nm, caused, for example, by the intensity from the 60% reflectivity factory calibration target exceeding the linear range of the CCD array. This can be avoided by using a darker factory calibration target in the factory NIST target test 314, for example, a target having a known diffuse reflectance from about 10% to about 30%.

[0291] Results from the factory/PM custom target test 312, the factory/PM NIST target test 314, and the pre-patient custom target test 330 provide the correction factors shown in blocks 324, 326, and 334, respectively used in preprocessing reflectance data from a patient scan using the BB1 white light source 188 and the BB2 white light source 190 shown in FIGS. 4, 7, and 8. Correction factors in block 324 represent background-subtracted, power-monitor-corrected (power-corrected), and null-target-subtracted reflectance data from a given factory/PM custom target test 312 (cp) and are shown in Equations 30 and 31:
FCCTMMBB1=<I_cp,BB1(i,λ,t_o)>_{i, masked}−FCNULLBB1  (30)
FCCTMMBB2=<I_cp,BB2(i,λ,t_o)>_{i, masked}−FCNULLBB2  (31)
where FCNULLBB1 and FCNULLBB2 are given by Equations 4 and 5, and < >_i,masked represents a one-dimensional array of mean data computed on a pixel-by-pixel basis in regions of area 445 of the scan pattern 444 of FIG. 20.

[0292] Correction factors in block 326 of FIG. 10 represent ratios of background-subtracted, power-corrected, and null-target-subtracted reflectance data from a factory/PM custom target test 312 (cp) and a factory/PM NIST standard target test 314 (fc) and are shown in Equations 32, 33, and 34:

[0293] $\begin{array}{cc}\mathrm{FCBREF}1\left[\right]=\frac{{\langle {I_{\mathrm{fc},\mathrm{BB1}}\left(i,\lambda ,t_o\right)}_{\mathrm{avg}\mathrm{of}4}-\mathrm{FCNULLBB}1\rangle }_{i,}}{{I_{\mathrm{fc},\mathrm{BB1}}\left(i,\lambda ,t_o\right)}_{\mathrm{avg}\mathrm{of}4}-\mathrm{FCNULLBB}1}& \left(32\right)\\ \mathrm{FCBREF}2\left[\right]=\frac{{\langle {I_{\mathrm{fc},\mathrm{BB2}}\left(i,\lambda ,t_o\right)}_{\mathrm{avg}\mathrm{of}4}-\mathrm{FCNULLBB}2\rangle }_{i,}}{{I_{\mathrm{fc},\mathrm{BB2}}\left(i,\lambda ,t_o\right)}_{\mathrm{avg}\mathrm{of}4}-\mathrm{FCNULLBB}2}& \left(33\right)\\ \mathrm{CALREF}=[0.5·\left(\mathrm{FCCTMBB}1/{\langle \mathrm{FCBREF}1\left[\right]\rangle }_{i,}\right)+& \left(34\right)\\ {\left(\mathrm{FCCTMBB}2/{\langle \mathrm{FCBREF}2\left[\right]\rangle }_{i,}\right)]}_{\mathrm{interp},\mathrm{fit}}& \end{array}$
where values of the two-dimensional arrays I_fc,BB1 and I_fc,BB2 are averages of data using the target at each of four positions, rotated 90° between each position; and all divisions, subtractions, and multiplications are on a location-by-location, pixel-by-pixel basis. The correction factor, CALREF, is a one-dimensional array of values of the quantity in brackets [ ] on the right side of Equation 34, interpolated such that they correspond to wavelengths at 1-nm intervals between λ=360 nm and λ=720 nm. The interpolated values are then fit with a quadratic or other polynomial to reduce noise.

[0294] Correction factors in block 334 of FIG. 10 represent background-subtracted, power-corrected, internal-stray-light-corrected reflectance data from a pre-patient custom target test 330 (cp) and are given in Equations 35 and 36 as follows:
BREFMBB1=<I_cp,BB1(i,λ,t′)−SLBB1>_i  (35)
BREFMBB2=<I_cp,BB2(i,λ,t′)−SLBB2>_i  (36)
where SLBB1 and SLBB2 are as shown in Equations 19 and 20.

[0295] Steps #4, 5, and 6 in each of blocks 342 and 344 of the spectral data pre-processing block diagram 340 of FIG. 11 include processing patient reflectance data using the correction factors from blocks 324, 326, and 334 of FIG. 10 computed using results of the factory/PM custom target test 312, the factory/PM NIST standard target test 314, and the pre-patient custom target test 330.

[0296] In step #4 of block 342 in FIG. 11, the array of background-subtracted, power-corrected, internal-stray-light-subtracted patient reflectance data obtained using the BB1 light source is multiplied by the two-dimensional array correction factor, FCBREF1[ ], and then in step #5, is divided by the correction factor BREFMBB1. After filtering using, for example, a 5-point median filter and a second-order 27-point Savitsky-Golay filter, the resulting array is linearly interpolated using results of the wavelength calibration step 302 in FIG. 10 to produce a two-dimensional array of spectral data corresponding to wavelengths ranging from 360 nm to 720 nm in 1-nm increments at each of 499 interrogation points of the scan pattern 202 shown in FIG. 5. This array is multiplied by CALREF in step #6 of block 342 in FIG. 11, and pre-processing of the BB1 spectral data in this embodiment is complete.

[0297] Steps #4, 5, and 6 in block 344 of FIG. 11 concern processing of BB2 data and is directly analogous to the processing of BB1 data discussed above.

[0298] Steps #4 and 5 in block 346 of FIG. 1 include processing fluorescence data using factory/PM-level correction factors, applied after background correction (step #1), power monitor correction (step #2), and stray light correction (step #3) of fluorescence data from a test sample. Steps #4 and 5 include application of correction factors sFCDYE and IRESPONSE, which come from the factory/PM fluorescent dye cuvette test 306 and the factory/PM tungsten source test 308 in FIG. 10.

[0299] The factory/PM tungsten source test 308 accounts for the wavelength response of the collection optics for a given instrument unit. The test uses a quartz tungsten halogen lamp as a light source. Emission from the tungsten filament approximates a blackbody emitter. Planck's radiation law describes the radiation emitted into a hemisphere by a blackbody (BB) emitter:
W_BB(λ)=[a·(CE)]/[λ⁵·{exp(b/λT)−1}]  (37)
where a=2πhc²=3.742×10¹⁶[W(nm)⁴/cm²]; b=hc/k=1.439×10⁷[(nm)K]; T is source temperature; CE is a fitted parameter to account for collection efficiency; and both T and CE are treated as variables determined for a given tungsten lamp by curve-fitting emission data to Equation 37.

[0300] The lamp temperature, T, is determined by fitting NIST-traceable source data to Equation 37. FIG. 23 shows a graph 582 depicting the spectral irradiance 584, W_{NIST lamp}, of a NIST-traceable quartz-tungsten-halogen lamp, along with a curve fit 590 of the data to the model in Equation 37 for blackbody irradiance, W_BB. Since the lamp is a gray-body and not a perfect blackbody, Equation 37 includes a proportionality constant, CE. This proportionality constant also accounts for the “collection efficiency” of the setup in an instrument 102 as depicted in the tissue characterization system 100 of FIG. 1. In the illustrative embodiment, the target from which measurements are obtained is about 50-cm away from the lamp and has a finite collection cone that subtends a portion of the emission hemisphere of the lamp. Thus, while W_BB(λ) in Equation 37 has units of [W/nm], calibration values for a given lamp used in the instrument 102 in FIG. 1 has units of [W/cm²-nm at 50 cm distance]. The two calibration constants, CE and T, are obtained for a given lamp by measuring the intensity of the given lamp relative to the intensity of a NIST-calibrated lamp using Equation 38:
W_lamp[I_lamp/I_{NIST lamp}]·W_{NIST lamp}  (38)
Then, values of T and CE are determined by plotting W_lamp versus wavelength and curve-fitting using Equation 37. The curve fit provides a calibrated lamp response, I_lamp(λ), to which the tungsten lamp response measured during factory/PM testing 308 at a given interrogation point and using a given instrument, S_lamp(i,λ), is compared. This provides a measure of “instrument response”, IR(i,λ), for the given point and the given instrument, as shown in Equation 39:
IR(i,λ)=S_lamp(i,λ)/I_lamp(λ)  (39)

[0301] The factory/PM tungsten source test 308 in FIG. 10 includes collecting an intensity signal from the tungsten lamp as its light reflects off an approximately 99% reflective target. The test avoids shadowing effects by alternately positioning the tungsten source at each of two locations—for example, on either side of the probe head 192 at locations corresponding to the white light source locations 188, 190 shown in FIG. 8—and using the data for each given interrogation point corresponding to the source position where the given point is not in shadow.

[0302] Once the instrument response measure, IR(i,λ), is obtained, a correction factor is determined such that its value is normalized to unity at a given wavelength, for example, at λ=500 nm. Thus, the distance between the lamp and the detecting aperture, the photoelectron quantum efficiency of the detector, and the reflectivity of the target do not need to be measured.

[0303] According to the illustrative embodiment, the fluorescence component of the spectral data pre-processing 114 of the system 100 of FIG. 1 corrects a test fluorescence intensity signal, S_F(i,λ), for individual instrument response by applying Equation 40 to produce I_F(i,λ), the instrument-response-corrected fluorescence signal:
I_F(i,λ)=S_F(i,λ)÷[{500·IR(i,λ)}/{λ·IR(i,500)}]  (40)
where IR(i,500) is the value of the instrument response measure IR at point i and at wavelength λ=500 nm; and where the term λ/500 converts the fluorescence intensity from energetic to photometric units, proportional to fluorophore concentration. In one embodiment, the differences between values of IR at different interrogation points is small, and a mean of IR(λ) over all interrogation points is used in place of IR(i,λ) in Equation 40.

[0304] The fluorescent dye cuvette test 306 accounts for variations in the efficiency of the collection optics 200 of a given instrument 102. Fluorescence collection efficiency depends on a number of factors, including the spectral response of the optics and detector used. In one embodiment, for example, the collection efficiency tends to decrease when a scan approaches the edge of the optics. A fluorescent dye cuvette test 306, performed as part of factory and/or preventive maintenance (PM) calibration, provides a means of accounting for efficiency differences.

[0305] An about 50-mm-diameter cuvette filled with a dye solution serves as a target for the fluorescent dye cuvette test 306 to account for collection optic efficiency variation with interrogation point position and variation between different units. The factory/PM dye-filled cuvette test 306 includes obtaining the peak intensity of the fluorescence intensity signal at each interrogation point of the dye-filled cuvette, placed in the calibration target port of the instrument 102, and comparing it to a mean peak intensity of the dye calculated for a plurality of units.

[0306] Illustratively, a calibrated dye cuvette can be prepared as follows. First, the fluorescence emission of a 10-mm-pathlength quartz cuvette filled with ethylene glycol is obtained. The ethylene glycol is of 99+% spectrophotometric quality, such as that provided by Aldrich Chemical Company. The fluorescence emission reading is verified to be less than about 3000 counts, particularly at wavelengths near the dye peak intensity. An approximately 2.5×10⁻⁴ moles/L solution of coumarin-515 in ethylene glycol is prepared. Coumarin-515 is a powdered dye of molecular weight 347, produced, for example, by Exciton Chemical Company. The solution is diluted with ethylene glycol to a final concentration of about 1.2×10⁻⁵ moles/L. Then, a second 10-mm-pathlength quartz cuvette is filled with the coumarin-515 solution, and an emission spectrum is obtained. The fluorescence emission reading is verified to have a maximum between about 210,000 counts and about 250,000 counts. The solution is titrated with either ethylene glycol or concentrated courmarin-515 solution until the peak lies in this range. Once achieved, 50-mm-diameter quartz cuvettes are filled with the titrated standard solution and flame-sealed.

[0307] A correction factor for fluorescence collection efficiency can be determined as follows. First, the value of fluorescence intensity of an instrument-response-corrected signal, I_F(i,λ), is normalized by a measure of the UV light energy delivered to the tissue as in Equation 41:
F_T(i,λ)=[I_F(i,λ)/P_m(i)]·[P_m/E_μj]_FC/PM  (41)
where F_T(i,λ) is the instrument-response-corrected, power-monitor-corrected fluorescence intensity signal; P_m(i) is a power-monitor reading that serves as an indirect measure of laser energy, determined by integrating or adding intensity readings from pixels on a CCD array corresponding to a portion on which a beam of the output laser light is directed; and [P_m/E_μj ]_FC/PM is the ratio of power monitor reading to output laser energy determined during factory calibration and/or preventive maintenance (FC/PM).

[0308] Next, the illustrative embodiment includes obtaining the fluorescence intensity response of a specific unit at a specific interrogation point (region) in its scan pattern using a cuvette of the titrated coumarin-515 dye solution as the target, and comparing that response to a mean fluorescence intensity response calculated for a set of units, after accounting for laser energy variations as in Equation 41. Equation 42 shows a fluorescence collection efficiency correction factor for a given unit applied to an instrument-response-corrected fluorescence signal, I_F(i,λ), along with the energy correction of Equation 41:

[0309] $\begin{array}{cc}\begin{array}{c}{F}_T\left(i,\lambda \right)=\frac{I_F\left(i,\lambda \right)}{P_m\left(i\right)}·{\left(\frac{P_m}{E_{\mu J}}\right)}_{\mathrm{PM}}·\\ {\left(\frac{{\langle \frac{I_{\mathrm{Dye}}\left(251,{\lambda }_p\right)}{P_m\left(251\right)}·\frac{P_m}{E_{\mathrm{uJ}}}\rangle }_{\mathrm{Instruments}}}{\frac{I_{\mathrm{Dye}}\left(i,{\lambda }_p\right)}{P_m\left(i\right)}·\frac{P_m}{E_{\mu J}}}\right)}_{\mathrm{PM}}\end{array}& \left(42\right)\end{array}$
where I_Dye(i,λ) is the peak measured fluorescence intensity at interrogation position i using the dye-filled cuvette, as shown in FIG. 3 1; λ_p is the wavelength (or its approximate pixel index equivalent) corresponding to the peak intensity; and the quantity in brackets < >_Instruments is the mean power-corrected intensity at interrogation point 251, corresponding to the center of the exemplary scan pattern of FIG. 5, calculated for a plurality of units.

[0310] The fluorescence collection efficiency tends to decrease when the scans approach the edge of the optics. FIG. 24 shows typical fluorescence spectra from the dye test 306. The graph 614 in FIG. 24 depicts as a function of wavelength 618 the fluorescence intensity 616 of the dye solution at each region of a 499-point scan pattern. The curves 620 all have approximately the same peak wavelength, λ_p, but the maximum fluorescence intensity values vary.

[0311] FIG. 25 shows how the peak fluorescence intensity (intensity measured at pixel 131 corresponding approximately to λ_p) 624, determined in FIG. 24, varies as a function of scan position (interrogation point) 626. Oscillations are due at least in part to optic scanning in the horizontal plane, while the lower frequency frown pattern is due to scan stepping in the vertical plane. According to the illustrative embodiment, curves of the fluorescence intensity of the dye cuvette at approximate peak wavelength are averaged to improve on the signal-to-noise ratio.

[0312] Equation 42 simplifies to Equations 43 and 44 as follows:

[0313] $\begin{array}{cc}{F}_T\left(i,\lambda \right)=\frac{I_F\left(i,\lambda \right)}{P_m\left(i\right)}·{\left(\frac{{\langle \frac{I_{\mathrm{Dye}}\left(251,{\lambda }_p\right)}{P_m\left(251\right)}·\frac{P_m}{E_{\mathrm{uJ}}}\rangle }_{\mathrm{Instruments}}}{\frac{I_{\mathrm{Dye}}\left(i,{\lambda }_p\right)}{P_m\left(i\right)}}\right)}_{\mathrm{PM}}& \left(43\right)\\ =\frac{I_F\left(i,\lambda \right)}{P_m\left(i\right)}·\mathrm{FCDYE}\left(i\right)& \left(44\right)\end{array}$
The term, [P_m/E_μj ]_PM, drops out of equation 42. Variations in laser energy measurements become less important as the energy is averaged over multiple measurements made on many instruments.

[0314] In FIG. 10, the correction factor sFCDYE in block 318 is a one-dimensional scalar array and is calculated using Equation 45:

[0315] $\begin{array}{cc}\mathrm{sFCDYE}=\left(\frac{{\langle \frac{I_{\mathrm{Dye}}\left(251,{\lambda }_p\right)}{P_m\left(251\right)}·\frac{P_m}{E_{\mathrm{uJ}}}\rangle }_{\mathrm{Instruments}}}{\frac{I_{\mathrm{Dye}}\left(i,{\lambda }_p\right)}{P_m\left(i\right)}}\right)& \left(45\right)\end{array}$
Here, values of I_Dye(i,λ_p) are background-subtracted, power-corrected, and null-target-subtracted.

[0316] In FIG. 10, the correction factor IRESPONSE in block 320 is a one-dimensional array and is calculated using the results of the factory/PM tungsten source test 308, as in Equation 46:
IRESPONSE=[{500·IR(i,λ)}/{λ·IR(i,500)}]  (46)
where IR(i,500) is the value of the instrument response measure IR given in Equation 39 at point i and at wavelength λ=500 nm.

[0317] Steps #4 and 5 in block 346 of the fluorescence spectral data pre-processing block diagram 340 of FIG. 11 include processing fluorescence data using sFCDYE and IRESPONSE as defined in Equations 45 and 46. The fluorescence data pre-processing proceeds by background-subtracting, power-correcting, and stray-light-subtracting fluorescence data from a test sample using Bkgnd[ ], sPowerMonitor[ ], and SLFL as shown in Steps #1, 2, and 3 in block 346 of FIG. 11. Then, the result is multiplied by sFCDYE and divided by IRESPONSE on a pixel-by-pixel, location-by-location basis. Next, the resulting two-dimensional array is smoothed using a 5-point median filter, then a second-order, 27-point Savitsky-Golay filter, and interpolated using the pixel-to-wavelength conversion determined in block 302 of FIG. 10 to produce an array of data corresponding to a spectrum covering a range from 360 nm to 720 nm at 1-nm intervals, for each of 499 interrogation points of the scan pattern.

[0318] As a further feature, the stability of fluorescence intensity readings are monitored between preventive maintenance procedures. This may be performed prior to each patient scan by measuring the fluorescence intensity of the center plug 438 of the custom target 426 shown in FIG. 19 and comparing the result to the expected value from the most recent preventive maintenance test. If the variance from the expected value is significant, and/or if the time between successive preventive maintenance testing is greater than about a month, the following correction factor may be added to those in block 346 of FIG. 11:

[0319] $\begin{array}{cc}\mathrm{FSTAB}=\frac{{\left[\frac{I_{\mathrm{ct}}\left(251,{\lambda }_p\right)}{P_m\left(251\right)}\right]}_{\mathrm{PM}}}{{\left[\frac{I_{\mathrm{ct}}\left(251,{\lambda }_p\right)}{P_m\left(251\right)}\right]}_{\mathrm{PP}}}& \left(47\right)\end{array}$
where PM denotes preventive maintenance test results; PP denotes pre-patient test results; I_ct(251, λ_p) is the fluorescence peak intensity reading at scan position 251 (center of the custom target) at peak wavelength λ_p; and P_m is the power monitor reading at scan position 251.

[0320] The spectral data pre-processing 114 in FIG. 11 further includes a procedure for characterizing noise and/or applying a threshold specification for acceptable noise performance. Noise may be a significant factor in fluorescence spectral data measurements, particularly where the peak fluorescence intensity is below about 20 counts/μJ (here, and elsewhere in this specification, values expressed in terms of counts/μJ are interpretable in relation to the mean fluorescence of normal squamous tissue being 70 ct/μJ at about 450 nm).

[0321] The procedure for characterizing noise includes calculating a power spectrum for a null target background measurement. The null target background measurement uses a null target having about 0% reflectivity, and the measurement is obtained with internal lights off and optionally with all external lights turned off so that room lights and other sources of stray light do not affect the measurement. Preferably, the procedure includes calculating a mean null target background spectrum of the individual null target background spectra at all interrogation points on the target—for example, at all 499 points of the scan pattern 202 of FIG. 5. Then, the procedure subtracts the mean spectrum from each of the individual null target background spectra and calculates the Fast Fourier Transform (FFT) of each mean-subtracted spectrum. Then, a power spectrum is calculated for each FFT spectrum and a mean power spectrum is obtained.

[0322] FIG. 26 shows a graph 678 depicting exemplary mean power spectra for various individual instruments 684, 686, 688, 690, 692, 694, 696. A 27-point Savitzky-Golay filter has an approximate corresponding frequency of about 6300 s⁻¹ and frequencies above about 20,000 s⁻¹ are rapidly damped by applying this filter. In the case of a 27-point Savistzky-Golay filter, spectral data pre-processing in FIG. 11 further includes applying a threshold maximum criterion of 1 count in the power spectrum for frequencies below 20,000 s⁻¹. Here, data from an individual unit must not exhibit noise greater than 1 count at frequencies below 20,000 s⁻¹ in order to satisfy the criterion. In FIG. 26, the criterion is not met for units with curves 692 and 696, since their power spectra contain points 706 and 708, each exceeding 1 count at frequencies below 20,000 s⁻¹. The criterion is met for all other units.

[0323] According to an alternative illustrative embodiment, a second noise criterion is applied instead of or in addition to the aforementioned criterion. The second criterion specifies that the mean power spectral intensity for a given unit be below 1.5 counts at all frequencies. In FIG. 26, the criterion is not met for units with curves 692 and 696, since their power spectra contain points 700 and 702, each exceeding 1.5 counts.

[0324] The illustrative spectral data pre-processing 114 in FIG. 11 and/or the factory/PM 110 and pre-patient calibration 116 and correction in FIG. 10 further includes applying one or more validation criteria to data from the factory/PM 110 and pre-patient 114 calibration tests. The validation criteria identify possibly-corrupted calibration data so that the data are not incorporated in the core classifier algorithms and/or the spectral masks of steps 132 and 130 in the system 100 of FIG. 1. The validation criteria determine thresholds for acceptance of the results of the calibration tests. According to the illustrative embodiment, the system 100 of FIG. 1 signals if validation criteria are not met and/or prompts retaking of the data.

[0325] Validation includes validating the results of the factory/PM NIST 60% diffuse reflectance target test 314 in FIG. 10. Validation may be necessary, for example, because the intensity of the xenon lamp used in the test 314 oscillates during a scan over the 25-mm scan pattern 202 of FIG. 5. The depth of modulation of measured reflected light intensity depends, for example, on the homogeneity of the illumination source at the target, as well as the collection efficiency over the scan field. The depth of modulation also depends on how well the target is aligned relative to the optical axis. In general, inhomogeneities of the illumination source are less important than inhomogeneities due to target misalignment, since illumination source inhomogeneities are generally accounted for by taking the ratio of reflected light intensity to incident light intensity. Thus, the calibration 110, 1 16 methods use one or two metrics to sense off-center targets and prompt retaking of data.

[0326] One such metric includes calculating a coefficient of variation, CV_i(λ), of measured reflected light intensity across the scan field according to Equation 48:

[0327] $\begin{array}{cc}{\mathrm{CV}}_i\left(\lambda \right)=\frac{{\mathrm{std}\left(I\left(\lambda ,i\right)\right)}_i}{{\mathrm{mean}\left(I\left(\lambda ,i\right)\right)}_i}& \left(48\right)\end{array}$
where I(λ,i)=mean [{I_target(λ,i)−I_bkg(λ,i)}/P_m(i)]_{4 rotations}; “std” represents standard deviation; i represents an interrogation point; λ represents wavelength (in one embodiment, between 370 nm and 700 nm); and P_m(i) represents the power monitor value for interrogation point i. I(λ,i) is the mean of the background-subtracted (bkg), power-monitor-corrected reflectance intensity values from the NIST target measured 4 times, rotating the target 90° between each measurement. Validation according to the metric of Equation 48 requires the value of CV_i(λ) be less than an experimentally-determined, fixed value.

[0328] Another metric from the 60% diffuse target test 314 includes calculating the relative difference, RD, between the minimum and maximum measured intensity over the scan field according to Equation 49:

[0329] $\begin{array}{cc}\mathrm{RD}\left(\lambda \right)=\frac{2·\left[{\max \left(I^{\prime }\left(\lambda ,i\right)\right)}_i-{\min \left(I^{\prime }\left(\lambda ,i\right)\right)}_i\right]}{\left[{\max \left(I^{\prime }\left(\lambda ,i\right)\right)}_i+{\min \left(I^{\prime }\left(\lambda ,i\right)\right)}_i\right]}& \left(49\right)\end{array}$
where

[0330] $I^{\prime }\left(\lambda ,i\right)={\mathrm{mean}\left(\left(\frac{I_{\mathrm{target}}\left(\lambda ,i\right)-I_{\mathrm{bkg}}\left(\lambda ,i\right)}{P_m\left(i\right)}\right)·{\mathrm{mean}\left(P_m\left(i\right)\right)}_i\right)}_{4\mathrm{rotations}}.$
_4rotations. Here, I′ is scaled by the mean of the power monitor values. In one embodiment, the relative difference, RD, between the minimum and maximum computed in Equation 49 is more sensitive to off-centered targets than the coefficient of variation, CV_i, computed in Equation 48. Here, validation requires the value of RD(λ) be less than an experimentally-determined, fixed value. In the illustrative embodiment, validation requires that Equation 50 be satisfied as follows:
RD(λ)<0.7 for λ between 370 nm and 700 nm  (50)
where RD(λ) is given by Equation 49.

[0331] Validation also includes validating the results of the tungsten source test 308 from FIG. 11 using the approximately 99% diffuse reflectivity target. This test includes obtaining two sets of data, each set corresponding to a different position of the external tungsten source lamp. Data from each set that are not affected by shadow are merged into one set of data. Since the power monitor correction is not applicable for this external source, a separate background measurement is obtained.

[0332] The illustrative calibration methods 110, 116 use one or two metrics to validate data from the tungsten source test 308. One metric includes calculating a coefficient of variation, CV_i(λ), of the mean foreground minus the mean background data, W(λ,i), of the merged set of data, as in Equation 51:

[0333] $\begin{array}{cc}{\mathrm{CV}}_i\left(\lambda \right)=\frac{{\mathrm{std}\left(W\left(\lambda ,i\right)\right)}_i}{{\mathrm{mean}\left(W\left(\lambda ,i\right)\right)}_i}& \left(51\right)\end{array}$
where the coefficient of variation, CV_i(λ), is calculated using the mean instrument spectral response curve, IR, averaging over all interrogation points of the scan pattern. Validation requires the value of CV_i(λ) be less than an experimentally-determined, fixed value. In the illustrative embodiment, validation requires that Equation 52 be satisfied for all interrogation points i:
CV_i(λ)<0.5 for λ between 370 nm and 700 nm  (52)
where CV_i(λ) is given by Equation 51.

[0334] A second metric includes calculating a mean absolute difference spectrum, MAD(λ), comparing the current spectral response curve to the last one measured, as in Equation 53:
MAD(λ)=mean (|IR_t(i,λ)−IR_t-1(i,λ)|)_i  (53)
where the instrument spectral response curve, IR, is given by Equation 39. Validation requires the value of MAD(λ) be less than an experimentally-determined, fixed value. In one embodiment, validation requires that Equation 54 be satisfied:
MAD(λ)<0.2 for λ between 370 nm and 700 nm  (54)
where MAD(λ) is given by Equation 53.

[0335] Validation can further include validating the results of the fluorescent dye cuvette test 306 in FIG. 10, used to standardize fluorescence measurements between individual units and to correcting for variation in collection efficiency as a unit collects data at interrogation points of a scan pattern. The illustrative calibration methods 110, 116 use one or more metrics to validate data from the fluorescent dye cuvette test 306 using a coefficient of variation, CV_i(λ), of dye cuvette intensity, I_Dye, as in Equation 55:

[0336] $\begin{array}{cc}{\mathrm{CV}}_i\left(\lambda \right)=\frac{{\mathrm{std}\left(I_{\mathrm{Dye}}\left(\lambda ,i\right)\right)}_i}{{\mathrm{mean}\left(I_{\mathrm{Dye}}\left(\lambda ,i\right)\right)}_i}& \left(55\right)\end{array}$

[0337] The coefficient of variation, CV_i(λ), in Equation 55 between about 470 mn and about 600 nm is generally representative of fluorescence efficiency variations over the scan pattern. The coefficient of variation at about 674 nm is a measure of how well the collection system blocks the 337-nm excitation light. As the excitation light passes over the surface of the cuvette, the incidence and collection angles go in and out of phase, causing modulation around 574 nm. The coefficient of variation at about 425 nm is a measure of the cleanliness of the cuvette surface and is affected by the presence of fingerprints, for example. The coefficient of variation below about 400 nm and above about 700 nm is caused by a combination of the influence of 337-nm stray excitation light and reduced signal-to-noise ratio due to limited fluorescence from the dye solution at these wavelengths.

[0338] One metric includes calculating a mean coefficient of variation, CV_i(λ), according to Equation 55, between about 500 nm and about 550 nm, and comparing the mean coefficient of variation to an experimentally-determined, fixed value. According to the illustrative embodiment, validation requires that Equation 56 be satisfied:
mean CV_i(λ)<0.06 for λ between 500 nm and 550 nm  (56)

[0339] A second metric includes requiring the coefficient of variation at about 674 nm be less than an experimentally-determined, fixed value. In one embodiment, validation requires that Equation 57 be satisfied for all interrogation points i:
CV_i(674)<0.5  (57)
where CV_i(λ) is calculated as in Equation 55.

[0340] Validation can also include validating results of the fluorescent dye cuvette test 306 using both Equations 56 and 57. Here, applying Equation 56 preyents use of data from tests where the scan axis is significantly shifted relative to the center of the optical axis, as well as tests where the cuvette is not full or is off-center. Applying Equation 57 prevents use of data from tests where a faulty UV emission filter is installed or where the UV filter degrades over time, for example.

[0341] Validation can also include validating the results of the 10% diffuse reflectivity custom target tests 312, 330 in FIG. 10. Here, an off-center target may result in a faulty test due to interference at regions near the edge of the target, as well as regions near the fluorescent and phosphorescent plugs that are improperly masked. According to the illustrative embodiment, validation of the custom target tests 312, 330 requires that the relative difference between the minimum and maximum intensity, RD(λ), is below a pre-determined value, where RD(λ) is calculated as in Equation 58:

[0342] $\begin{array}{cc}\mathrm{RD}\left(\lambda \right)=\frac{2·\left[{\max \left(I^{\prime }\left(\lambda ,i\right)\right)}_{i=\mathrm{mask}}-{\min \left(I^{\prime }\left(\lambda ,i\right)\right)}_{i=\mathrm{mask}}\right]}{\left[{\max \left(I^{\prime }\left(\lambda ,i\right)\right)}_{i=\mathrm{mask}}+{\min \left(I^{\prime }\left(\lambda ,i\right)\right)}_{i=\mathrm{mask}}\right]}& \left(58\right)\end{array}$
where (I′(λ,i))_i=mask refers to all scan positions except those masked to avoid the plugs, as shown in FIGS. 19 and 20. In one embodiment, validation requires that Equation 59 be satisfied:
RD(λ)<1.2 for λ between 370 nm and 700 nm  (59)
where RD(λ) is calculated as in Equation 58.

[0343] The invention can also validate the results of the null target test 304, 328 in FIG. 10. The null target test is used, for example, to account for internal stray light in a given instrument. According to the illustrative embodiment, a maximum allowable overall amount of stray light is imposed. For example, in one preferred embodiment, validation of a null target test 304, 328 requires the integrated energy, IE, be below a predetermined value, where IE is calculated from background-subtracted, power-monitor-corrected null target reflectance intensity measurements, as in Equation 60:

[0344] $\begin{array}{cc}\begin{array}{c}\mathrm{IE}={\int }_{370}^{700}{\mathrm{mean}\left(\frac{\mathrm{null}\left(\lambda ,i\right)-\mathrm{bkg}\left(\lambda ,i\right)}{P_m\left(i\right)}\right)}_i·{\mathrm{mean}\left(P_m\left(i\right)\right)}_{i}d\lambda \\ \approx \sum _{370}^{700}{\mathrm{mean}\left(\frac{\mathrm{null}\left(\lambda ,i\right)-\mathrm{bkg}\left(\lambda ,i\right)}{P_m\left(i\right)}\right)}_i·{\mathrm{mean}\left(P_m\left(i\right)\right)}_i\end{array}& \left(60\right)\end{array}$
where Δλ in the summation above is about 1-nm. In one embodiment, validation requires that Equation 61 be satisfied:
IE<4000 counts  (61)
where IE is calculated as in Equation 60.

[0345] The invention may also employ validation of the open air target test 310 in FIG. 10. Like the null target test 304, 328, the open air target test is used in accounting for internal stray light in a given instrument. According to the illustrative embodiment, validation of an open air target test 310 requires the integrated energy, IE, be below a predetermined value, where IE is calculated as in Equation 60, except using open air reflectance intensity measurements in place of null target measurements, null(λ,i). By way of example, in one case validation requires that the value of integrated energy for the open air test be below 1.2 times the integrated energy from the null target test, calculated as in Equation 60.

[0346] According to another feature, the invention validates the power monitor corrections used in the calibration tests in FIG. 10. Patient and calibration data that use a power monitor correction may be erroneous if the illumination source misfires. According to one approach, validation of a power monitor correction requires that the maximum raw power monitor intensity reading, P_m,max(i), be greater than a predetermined minimum value and/or be less than a predetermined maximum value at each interrogation point i. In the illustrative embodiment, validation requires that Equation 62 be satisfied:
pb 6000 counts<P_m,max(i)<30,000 counts for all i  (62)

[0347] According to the illustrative embodiment, spectral data pre-processing 114 in FIG. 11 includes accounting for the result of the real-time motion tracker 106 in the system 100 of FIG. 1 when applying the correction factors in block diagram 340 of FIG. 11. As discussed herein, the system 100 of FIG. 1 applies the calibration-based corrections in FIG. 11 to spectral data acquired from a patient scan. These corrections are applied by matching spectral data from each interrogation point in a patient scan to calibration data from a corresponding interrogation point. However, a patient scan of the 499 interrogation points shown in the scan pattern 202 of FIG. 5 takes approximately 12 seconds. During those 12 seconds, it is possible that the tissue will shift slightly, due to patient movement. Thus, spectral data obtained during a scan may not correspond to an initial index location, since the tissue has moved from its original position in relation to the scan pattern 202. The real-time motion tracker 106 of FIG. 1 accounts for this movement by using data from video images of the tissue to calculate, as a function of scan time, a translational shift in terms of an x-displacement and a y-displacement. The motion tracker 106 also validates the result by determining whether the calculated x,y translational shift accurately accounts for movement of the tissue in relation to the scan pattern or some other fixed standard such as the initial position of component(s) of the data acquisition system (the camera and/or spectroscope). The motion tracker 106 is discussed in more detail below.

[0348] Illustratively, the spectral data pre-processing 114 in FIG. 11 accounts for the result of the real-time motion tracker 106 by applying a calibration spectra lookup method. The lookup method includes obtaining the motion-corrected x,y coordinates corresponding to the position of the center of an interrogation point from which patient spectral data is obtained during a patient scan. Then the lookup method includes using the x,y coordinates to find the calibration data obtained from an interrogation point whose center is closest to the x,y coordinates.

[0349] The scan pattern 202 of FIG. 5 is a regular hexagonal sampling grid with a pitch (center-to-center distance) of 1.1 mm and a maximum interrogation point spot size of 1 mm. This center-to-center geometry indicates a horizontal pitch of 1.1 mm, a vertical pitch of about 0.9527 mm, and a maximum comer distance of the circumscribed regular hexagon to the center of 0.635 mm. Thus, the illustrative lookup method finds the calibration interrogation point whose center is closest to the motion-corrected x,y coordinates of a patient scan interrogation point by finding coordinates of a calibration point that is less than 0.635 mm from x,y.

[0350] The background spectra, Bkgnd[ ], in FIG. 11, are obtained at nearly the same time patient spectral data are obtained and no motion correction factor is needed to background-subtract patient spectral data. For example, at a given interrogation point during a patient scan, the system 100 of FIG. 1 pulses the UV light source on only while obtaining fluorescence data, then pulses the BB1 light source on only while obtaining the first set of reflectance data, then pulses the BB2 light source on only while obtaining the second set of reflectance data, then obtains the background data, Bkgnd[ ], at the interrogation point with all internal light sources off. All of this data is considered to be approximately simultaneous and no motion correction factor is needed for the Bkgnd[ ] calibration data.

[0351] The real-time motion tracker 106 of FIG. 1 uses video data obtained from the tissue contemporaneously with the spectral data. In addition to motion correction, the system of FIG. 1 uses video (image) data to determine image masks for disease probability computation, to focus the probe 142 through which spectral and/or image data is acquired, and to compute a brightness and contrast correction and/or image enhancement for use in disease overlay display.

[0352] FIG. 27A is a block diagram 714 showing steps an operator performs before a patient scan as part of spectral data acquisition 104 in the system 100 of FIG. 1, according to an illustrative embodiment of the invention. The steps in FIG. 27A are arranged sequentially with respect to a time axis 716. As shown, an operator applies a contrast agent to the tissue sample 718, marks the time application is complete 720, focuses the probe 142 through which spectral and/or image data will be obtained 722, then initiates the spectral scan of the tissue 724 within a pre-determined window of time.

[0353] According to the illustrative embodiment, the window of time is an optimum range of time following application of contrast agent to tissue within which an approximately 12 to 15 second scan can be performed to obtain spectral data that are used to classify tissue samples with a high degree of sensitivity and selectivity. The optimum window should be long enough to adequately allow for restarts indicated by focusing problems or patient movement, but short enough so that the data obtained is consistent. Consistency of test data is needed so that tissue classification results for the test data are accurate and so that the test data may be added to a bank of reference data used by the tissue classification scheme. In one illustrative embodiment, the optimum window is expressed in terms of a fixed quantity of time following application of contrast agent. In another illustrative embodiment, the optimum window is expressed in terms of a threshold or range of a trigger signal fronm the tissue, such as a reflectance intensity indicative of degree of tissue whiteness.

[0354] The contrast agent in FIG. 27A is a solution of acetic acid. According to one exemplary embodiment, the contrast agent is a solution between about 3 volume percent and about 6 volume percent acetic acid in water. More particularly, in one preferred embodiment, the contrast agent is an about 5 volume percent solution of acetic acid in water. Other contrast agents may be used, including, for example, formic acid, propionic acid, butyric acid, Lugol's iodine, Shiller's iodine, methylene blue, toluidine blue, indigo carmine, indocyanine green, fluorescein, and combinations of these agents.

[0355] According to the illustrative embodiment, the time required to obtain results from a patient scan, following pre-patient calibration procedures, is a maximum of about 5 minutes. Thus, in FIG. 27A, the five-minute-or-less procedure includes applying acetic acid to the tissue sample 726; focusing the probe (142 728; waiting, if necessary, for the beginning of the optimum pre-determined window of time for obtaining spectral data 730; obtaining spectral data at all interrogation points of the tissue sample 732; and processing the data using a tissue classification scheme to obtain a diagnostic display 734. The display shows, for example, a reference image of the tissue sample with an overlay indicating regions that are classified as necrotic tissue, indeterminate regions, healthy tissue (no evidence of disease, NED), and CIN ⅔ tissue, thereby indicating where biopsy may be needed.

[0356] The times indicated in FIG. 27A may vary. For example, if the real-time motion tracker 106 in the system of FIG. 1 indicates too much movement occurred during a scan 732, the scan 732 may be repeated if there is sufficient time left in the optimum window.

[0357] FIG. 27B is a block diagram 738 showing a time line for the spectral scan 732 indicated in FIG. 27A. In the embodiment shown in FIG. 27B, a scan of all interrogation points of the scan pattern (for example, the scan pattern 202 of FIG. 5) takes from about 12 seconds to about 15 seconds, during which time a sequence of images is obtained for motion tracking, as performed in step 106 of the system 100 of FIG. 1. By the time a scan begins, a motion-tracking starting image 742 and a target laser image 744 have been obtained 740. The target laser image 744 may be used for purposes of off-line focus evaluation, for example. During the acquisition of spectral data during the scan, a frame grabber 120 (FIG. 1) obtains a single image about once every second 746 for use in monitoring and/or correcting for movement of the tissue from one frame to the next. In FIG. 27B, a frame grabber acquires images 748, 750, 752, 754, 756, 758, 760, 762, 764, 766, 768 that are used to track motion that occurs during the scan.

[0358] Image data from a video subsystem is used, for example, in target focusing 728 in FIG. 27A and in motion tracking 106, 746 in FIG. 27B. Image data is also used in detecting the proper alignment of a target in a calibration procedure, as well as detecting whether a disposable is in place prior to contact of the probe with a patient. Additionally, in one embodiment, colposcopic video allows a user to monitor the tissue sample throughout the procedure.

[0359] FIG. 28 is a block diagram 770 that shows the architecture of an illustrative video subsystem used in the system 100 of FIG. 1. FIG. 28 shows elements of the video subsystem in relation to components of the system 100 of FIG. 1. The video subsystem 770 acquires single video images and real-time (streaming) video images. The video subsystem 770 can post-process acquired image data by applying a mask overlay and/or by adding other graphical annotations to the acquired image data. Illustratively, image data is acquired in two frame buffers during real-time video acquisition so that data acquisition and data processing can be alternated between buffers. The camera(s) 772 in the video subsystem 770 of FIG. 28 include a camera located in or near the probe head 192 shown in FIG. 4, and optionally includes a colposcope camera external to the probe 142 for visual monitoring of the tissue sample during testing. In one illustrative embodiment, only the probe head camera is used. FIG. 28 shows a hardware interface 774 between the cameras 772 and the rest of the video subsystem 770. The frame grabber 120 shown in FIG. 1 acquires video data for processing in other components of the tissue characterization system 100. In one embodiment, the frame grabber 120 uses a card for video data digitization (video capture) and a card for broadband illumination (for example, flash lamps) control. For example, one embodiment uses a Matrox Meteor 2 card for digitization and an Imagenation PXC-200F card for illumination control, as shown in block 776 of FIG. 28.

[0360] Real-time (streaming) video images are used for focusing the probe optics 778 as well as for visual colposcopic monitoring of the patient 780. Single video images provide data for calibration 782, motion tracking 784, image mask computation (used in tissue classification) 786, and, optionally, detection of the presence of a disposable 788. In some illustrative embodiments, a single reference video image of the tissue sample is used to compute the image masks 108 in the system 100 of FIG. 1. This reference image is also used in determining a brightness and contrast correction and/or other visual enhancement 126, and is used in the disease overlay display 138 in FIG. 1.

[0361] The illustrative video subsystem 770 acquires video data 790 from a single video image within about 0.5 seconds. The video subsystem 770 acquires single images in 24-bit RGB format and is able to convert them to grayscale images. For example, image mask computation 108 in FIG. 1 converts the RGB color triplet data into a single luminance value, Y, (grayscale intensity value) at each pixel, where Y is given by Equation 63:
Y=0.299R+0.587G+0.114B  (63)
where the grayscale intensity component, Y, is expressed in terms of red (R), green (G), and blue (B) intensities; and where R, G, and B range from 0 to 255 for a 24-bit RGB image.

[0362] Laser target focusing 728 is part of the scan procedure in FIG. 27A. An operator uses a targeting laser in conjunction with real-time video to quickly align and focus the probe 142 prior to starting a patient scan. In the illustrative embodiment, an operator performs a laser “spot” focusing procedure in step 728 of FIG. 27A where the operator adjusts the probe 142 to align laser spots projected onto the tissue sample. The user adjusts the probe while looking at a viewfinder with an overlay indicating the proper position of the laser spots. In one alternative embodiment, an operator instead performs a thin-line laser focusing method, where the operator adjusts the probe until the laser lines become sufficiently thin. The spot focus method allows for faster, more accurate focusing than a line-width-based focusing procedure, since thin laser lines can be difficult to detect on tissue, particularly dark tissue or tissue obscured by blood. Quick focusing is needed in order to obtain a scan within the optimal time window following application of contrast agent to the tissue; thus, a spot-based laser focusing method is preferable to a thin line method, although a thin line focus method may be used in alternative embodiments.

[0363] A target focus validation procedure 122 is part of the tissue characterization system 100 of FIG. 1, and determines whether the optical system of the instrument 102 is in focus prior to a patient scan. If the system is not in proper focus, the acquired fluorescence and reflectance spectra may be erroneous. Achieving proper focus is important to the integrity of the image masking 108, real-time tracking 106, and overall tissue classification 132 components of the system 100 of FIG. 1.

[0364] The focus system includes one or more target laser(s) that project laser light onto the patient sample prior to a scan. In one embodiment, the targeting laser(s) project laser light from the probe head 192 toward the sample at a slight angle with respect to the optical axis of the probe 142 so that the laser light that strikes the sample moves within the image frame when the probe is moved with respect to the focal plane. For example, in one illustrative embodiment, four laser spots are directed onto a target such that when the probe 142 moves toward the target during focusing, the spots move closer together, toward the center of the image. Similarly, when the probe 142 moves away from the target, the spots move further apart within the image frame, toward the comers of the image.

[0365] FIG. 29A is asingle video image 794 of atarget 796 of 10% diffuse io reflectivity upon which a target laser projects a focusing pattern of four laser spots 798, 800, 802, 804. During laser target focusing 728 (FIG. 27A), an operator views four focus rings that are displayed at predetermined locations, superimposed on the target focusing image. FIG. 29B depicts the focusing image 794 on the target 796 in FIG. 29A with superimposed focus rings 806, 808, 810, 812. The operator visually examines the relative positions of the laser spots 798, 800, 802, 804 in relation to the corresponding focus rings 806, 808, 810, 812 while moving the probe head 192 along the optical axis toward or away from the target/tissue sample. When the laser spots lie within the focus rings as shown in FIG. 29B, the system is within its required focus range. The best focus is achieved by aligning the centers of all the laser spots with the corresponding centers of the focus rings, Alternatively, spot patteins of one, two, three, five, or more laser spots may be used for focus alignment.

[0366] It is generally more difficult to align laser spots that strike a non-flat tissue sample target than to align the spots on a flat, uniform target as shown in FIG. 29B. In some instances, a laser spot projected onto tissue is unclear, indistinct, or invisible. Visual evaluation of focus may be subjective and qualitative. Thus, a target focus validation procedure is useful to insure proper focus of a tissue target is achieved. Proper focus allows the comparison of both image data and spectral data from different instrument units and different operators.

[0367] In one illustrative embodiment, the system 100 of FIG. 1 performs an automatic target focus validation procedure using a single focus image. The focus image is a 24-bit RGB color image that is obtained before acquisition of spectral data in a patient scan. The focus image is obtained with the targeting laser turned on and the broadband lights (white lights) turned off. Automatic target focus validation includes detecting the locations of the centers of visible laser spots and measuring their positions relative to stored, calibrated positions (“nominal” center positions). Then, the validation procedure applies a decision rule based on the number of visible laser spots and their positions and decides whether the system is in focus and a spectral scan can be started.

[0368] FIG. 30 is a block diagram 816 of a target focus validation procedure according to an illustrative embodiment of the invention. The steps include obtaining a 24-bit RGB focus image 818, performing image enhancement 820 to highlight the coloration of the laser spots, performing morphological image processing (dilation) to fill holes and gaps within the spots 822, defining a region of interest (ROI) of the image 824, and computing a mean and standard deviation 826 of the luminance values (brightness) of pixels within the region of interest. Next, the focus validation procedure iteratively and dynamically thresholds 828 the enhanced focus image using the computed mean and standard deviation to extract the laser spots. Between thresholding iterations, morphological processing 830 disconnects differentiated image objects and removes small image objects from the thresholded binary image, while a region analysis procedure 832 identifies and removes image objects located outside the bounds of the target laser spot pathways 838 and objects whose size and/or shape do not correspond to a target laser spot. After all thresholding iterations, the found “spots” are either verified as true target laser spots or are removed from the image 834, based on size, shape, and/or location. Next, in step 842, the focus validation procedure computes how far the centers of the found spots are from the nominal focus centers and converts the difference from pixels to millimeters in step 844. The validation procedure then applies a decision rule based on the number of found spots and their positions and decides whether the system is in focus such that a spectral scan of the patient can begin.

[0369] The focus validation procedure of FIG. 30 begins with obtaining the 24-bit RGB focus image and splitting it into R, G, and B channels. Each channel has a value in the range of 0 to 255. FIG. 31 depicts the RGB focus image 794 from FIG. 29A with certain illustrative geometry superimposed. FIG. 31 shows the four nominal spot focus centers 850, 852, 854, 856 as red dots, one of which is the red dot labeled “N” in quadrant 1. The nominal spot focus centers represent the ideal location of centers of the projected laser spots, achieved when the probe optics are in optimum focus. The nominal spot focus centers 850, 852, 854, 856 correspond to the centers of the rings 806, 808, 810, 812 in FIG. 29B. An (x,y) position is determined for each nominal focus center. A nominal image focus center (857), O, is defined by the intersection of the two red diagonal lines 858, 860 in FIG. 31. The red diagonal lines 858, 860 connect the two pairs of nominal spot focus centers 852, 854 in quadrants 2 and 3 and 850, 856 in quadrants 1 and 4, respectively. Also, the slopes of the two lines 858, 860 are computed for later use.

[0370] Step 820 in the procedure of FIG. 30 is image enhancement to highlight the coloration of the laser spots in contrast to the surrounding tissue. In one embodiment, the R value of saturated spots is “red clipped” such that if R is greater than 180 at any pixel, the R value is reduced by 50. Then, a measure of greenness, G_E, of each pixel is computed as in Equation 64:
G_E=G−R−15  (64)
where G is the green value of a pixel, R is the red value of the pixel, and 15 is a correction factor to remove low intensity noise, experimentally-determined here to be 15 gray levels.

[0371] FIG. 32A represents the green channel of an RGB image 864 of a cervical tissue sample, used in an exemplary target focus validation procedure. In this image, only two top focus laser spots 868, 870 are clear. The lower right spot 872 is blurred/diffused while the lower left spot 874 is obscured. The green-channel luminance (brightness), G_E, of the green-enhanced RGB image 864 of FIG. 32A may be computed using Equation 64 and may be displayed, for example, as grayscale luminance values between 0 and 255 at each pixel.

[0372] In step 822 of FIG. 30, the focus validation procedure performs morphological dilation using a 3×3 square structuring element to fill holes and gaps within the found spots. Then in step 824, the procedure uses a pre-defined, circular region of interest (ROI) for computing a mean, M, and a standard deviation, STD, 826 of the greenness value, G_E, of the pixels within the ROI, which are used in iterative dynamic thresholding 828. According to the illustrative embodiment, the ROI is a substantially circular region with a 460-pixel diameter whose center coincides with the nominal image focus center, O.

[0373] Before iterative dynamic thresholding begins, G_E is set equal to zero at a 50-pixel diameter border about the ROI. Then, iterative dynamic thresholding 828 begins by setting an iteration variable, p, to zero, then computing a threshold value, Th, as follows:
Th=M+p·STD  (65)
where M and STD are defined from the ROI. Since p=0 in the first iteration, the threshold, Th, is a “mean” greenness value over the entire ROI in the first iteration. In this embodiment, image thresholding is a subclass of image segmentation that divides an image into two segments. The result is a binary image made up of pixels, each pixel having a value of either 0 (off) or 1 (on). In step 828 of the focus validation procedure of FIG. 30, the enhanced greenness value of a pixel corresponding to point (x,y), within the ROI, G_E(x,y), is compared to the threshold value, Th. The threshold is applied as in Equation 66:
IF G_E(x,y)>Th, THEN the binary pixel value at (x,y), B_T=1, else B_T=0.  (66)

[0374] Iterative dynamic thresholding 828 proceeds by performing morphological opening 830 to separate nearby distinguishable image objects and to remove small objects of the newly thresholded binary image. According to the illustrative embodiment, the morphological opening 830 includes performing an erosion, followed by a dilation, each using a 3×3 square structuring element. The procedure then determines the centroid of each of the thresholded objects and removes each object whose center is outside the diagonal bands bounded by two lines that are 40 pixels above and below the diagonal lines 858, 860 in FIG. 31. These diagonal bands include the region between lines 876, 878 and the region between lines 880, 882 in FIG. 31, determined in step 838 of FIG. 30. An image object whose center lies outside these bands does not correspond to a target focus spot, since the centers of the focus laser-spots should appear within these bands at any position of the probe along the optical axis. The spots move closer together, within the bands, as the probe moves closer to the tissue sample, and the spots move farther apart, within the bands, as the probe moves away from the tissue sample.

[0375] Next, step 832 of the thresholding iteration 828 computes an area (A), eccentricity (E), and equivalent diameter (ED) of the found image objects, and removes an object whose size and/or shape—described here by A, E, and ED—does not correspond to that of a focus laser spot. E and ED are defined as follows:
E=(1−b²/a²)^0.5  (67)
ED=2(A/π)^0.5  (68)
where a is the minor axis length and b is the major axis length in units of pixels. For example, step 832 applies Equation 69 as follows:
IF A>5000 OR IF E>0.99 OR IF ED>110, THEN remove object (set B_T=0 for all pixels in object).  (69)
Other criteria may be applied. For example, Equation 70 may be applied in place of Equation 69:
IF A>2500 OR IF E>0.99 OR IF ED>80, THEN remove object (set B_T=0 for all pixels in object).  (70)

[0376] Next, the iteration variable, p, is increased by a fixed value, for example, by 0.8, and a new threshold is calculated using Equation 65. The iteration proceeds by applying the new threshold, performing a morphological opening, computing centroids of the newly thresholded regions, removing regions whose center position, size, and/or shape do not correspond to those of a target focus spot, and stepping up the value of the iteration variable p. Iterative dynamic thresholding proceeds until a predetermined condition is satisfied. For example, the thresholding ends when the following condition is satisfied:
IF p>6 OR IF the number ofqualified spots (image objects)≦4, THEN STOP.  (71)

[0377] Step 834 of the focus validation procedure eliminates any image object remaining after dynamic thresholding that does not meet certain laser spot size and shape criteria. For example, according to the illustrative embodiment, step 834 applies the condition in Equation 72 for each remaining image object:
IF A<80 OR IF E>0.85 OR IF ED<10, THEN remove object.  (72)

[0378] In an alternative embodiment, one or more additional criteria based on the position of each image object (found spot) are applied to eliminate objects that are still within the focus bands of FIG. 31, but are too far from the nominal centers 850, 852, 854, 856 to be valid focus spots.

[0379] FIG. 32B shows an image 898 of the cervical tissue sample of FIG. 32A following step 834, wherein the top two image objects were verified as target laser spots, while the bottom objects were eliminated.

[0380] Step 842 of the focus validation procedure assigns each of the found spots to its respective quadrant and computes the centroid of each found spot. FIG. 31 shows the found spots as blue dots 900, 902, 904, 906. Then for each found spot, step 842 computes the distance between the center of the spot to the nominal image focus center 857, O. For the focus spot center 900 labeled “F” in FIG. 31, this distance is L_OF, the length of the blue line 910 from point O to point F. The distance between the nominal focus center, N, 850 corresponding to the quadrant containing the found spot, and the nominal image focus center 857, O, is L_ON, the length of the red line 912 from point O to point N. Step 842 of the focus validation procedure then determines a focus value for verified focus spot 900 equal to the difference between the lengths L_OF and L_ON. The focus value of each of the verified focus spots is computed in this manner, and the focus values are converted from pixels to millimeters along the focus axis (z-axis) in step 844 of FIG. 30 using an empirically-determined conversion ratio—for example, 0.34 mm per pixel.

[0381] Next, the focus validation procedure of FIG. 30 applies a decision rule in step 846 based on the number of found spots and their positions. The decision rule is a quantitative means of deciding whether the system is in focus and a spectral scan of the tissue can begin. According to the illustrative embodiment, step 846 applies a decision rule given by Equations 73, 74, and 75:
IF 3 or more spots are found, THEN IF the focus value determined in step 842 is≦6 mm for any 3 spots OR IF the focus value is≦4 mm for any 2 spots, THEN “Pass”, ELSE “Fail” (require refocus).  (73)
IF only 2 spots are found, THEN IF the focus value of any spot is≧4 mm, THEN “Fail” (require refocus), ELSE “Pass”.  (74)
IF≦1 spot is found, THEN “Fail” (require refocus).  (75)
Other decision rules may be used alternatively.

[0382] FIGS. 33 and 34 show the application of the focus validation procedure of FIG. 30 using a rubber cervix model placed so that the two upper laser spots are within the os region. For this example, the distance between the edge of the probe head 192 and the target (or target tissue) is approximately 100 mm at optimum focus, and the distance light travels between the target (or target tissue) and the first optic within the probe 142 is approximately 130 mm at optimum focus.

[0383] FIG. 33 is a 24-bit RGB target laser focus image 942 of a rubber cervix model 944 onto which four laser spots 946, 948, 950, 952 are projected. The cervix model 944 is off-center in the image 942 such that the two upper laser spots 946, 948 lie within the os region. FIG. 34 shows a graph 954 depicting as a function of probe position relative to the target tissue 956, the mean of a focus value 958 (in pixels) of each of the four laser spots 946, 948, 950, 952 projected onto the rubber cervix model 944. The curve fit 960 of the data indicates the relationship between measured focus, f, 958 and probe location, z_p, 956 (in mm) is substantially linear. However, the curve is shifted down and is not centered at (0,0). This indicates a focus error introduced by the manual alignment used to obtain the z=0 focus position. Such an error may prompt a “Fail” determination in step 846 of the focus validation procedure of FIG. 30, depending on the chosen decision rule. FIG. 34 indicates the difficulty in making a visual focus judgment to balance the focus of the four spots, particularly where the target surface (tissue sample) is not flat and perpendicular to the optical axis (z-axis) of the probe system.

[0384] The focus validation procedure illustrated in FIG. 30 provides an automatic, quantitative check of the quality of focus. Additionally, in the illustrative embodiment, the focus validation procedure predicts the position of optimum focus and/or automatically focuses the optical system accordingly by, for example, triggering a galvanometer subsystem to move the probe to the predicted position of optimum focus.

[0385] The focus validation procedure in FIG. 30 produces a final decision in step 846 of “Pass” or “Fail” for a given focus image, based on the decision rule given by Equations 73-75. This indicates whether the focus achieved for this tissue sample is satisfactory and whether a spectral data scan may proceed as shown in step 732 of FIGS. 27A and 27B.

[0386] After application of contrast agent 726 and target focusing 728, step 730 of FIG. 27A indicates that the operator waits for the beginning of the optimum window for obtaining spectral data unless the elapsed time already exceeds the start of the window. The optimum window indicates the best time period for obtaining spectral data, following application of contrast agent to the tissue, considering the general time constraints of the entire scan process in a given embodiment. For example, according to the illustrative embodiment, it takes from about 12 to about 15 seconds to perform a spectral scan of 499 interrogation points of a tissue sample. An optimum window is determined such that data may be obtained over a span of time within this window from a sufficient number of tissue regions to provide an adequately detailed indication of disease state with sufficient sensitivity and selectivity. The optimum window preferably, also allows the test data to be used, in turn, as reference data in a subsequently developed tissue classification module. According to another feature, the optimum window is wide enough to allow for restarts necessitated, for example, by focusing problems or patient movement. Data obtained within the optimum window can be added to a bank of reference data used by a tissue classification scheme, such as component 132 of the system 100 of FIG. 1. Thus, the optimum window is preferably narrow enough so that data from a given region is sufficiently consistent regardless of when, within the optimum window, it is obtained.

[0387] According to the illustrative embodiment, the optimal window for obtaining spectral data in step 104 of FIG. 1 is a period of time from about 30 seconds following application of the contrast agent to about 130 seconds following application of the contrast agent. The time it takes an operator to apply contrast agent to the tissue sample may vary, but is preferably between about 5 seconds and about 10 seconds. The operator creates a time stamp in the illustrative scan procedure of FIG. 27A after completing application of the contrast agent, and then waits 30 seconds before a scan may begin, where the optimum window is between about 30 seconds and about 130 seconds following application of contrast agent. If the scan takes from about 12 seconds to about 15 seconds to complete (where no retake is required), the start of the scan procedure must begin soon enough to allow all the data to be obtained within the optimum window. In other words, in this embodiment, the scan must begin at least before 115 (assuming a worst case of 15 seconds to complete the scan) seconds following the time stamp (115 seconds after application of contrast agent) so that the scan is completed by 130 seconds following application of contrast agent. Other optimum windows may be used. In one embodiment, the optimum window is between about 30 seconds and about 110 seconds following application of contrast agent. One alternative embodiment has an optimal window with a “start” time from about 10 to about 60 seconds following application of acetic acid, and an “end” time from about 110 to about 180 seconds following application of acetic acid. Other optimum windows may be used.

[0388] In one illustrative embodiment, the tissue characterization system 100 of FIG. 1 includes identifying an optimal window for a given application, and/or subsequently using spectral data obtained within the pre-determined window in a tissue classification module, such as step 132 of FIG. 1. According to one feature, optimal windows are determined by obtaining optical signals from reference tissue samples with known states of health at various times following application of a contrast agent.

[0389] Determining an optimal window illustratively includes the steps of obtaining a first set of optical signals from tissue samples having a known disease state, such as CIN ⅔ (grades 2 and/or 3 cervical intraepithelial neoplasia); obtaining a second set of optical signals from tissue samples having a different state of health, such as non-CIN ⅔; and categorizing each optical signal into “bins” according to the tiime it was obtained in relation to the time of application of contrast agent. The optical signal may include, for example, a reflectance spectrum, a fluorescence spectrum, a video image intensity signal, or any combination of these.

[0390] A measure of the difference between the optical signals associated with the two types of tissue is then obtained, for example, by determining a mean signal as a function of wavelength for each of the two types of tissue samples for each time bin, and using a discrimination function to determine a weighted measure of difference between the two mean optical signals obtained within a given time bin. This provides a measure of the difference between the mean optical signals of the two categories of tissue samples—diseased and healthy—weighted by the variance between optical signals of samples within each of the two categories.

[0391] According to the illustrative embodiment, the invention further includes developing a classification model for each time bin for the purpose of determining an optimal window for obtaining spectral data in step 104 of FIG. 1. After determining a measure of difference between the tissue types in each bin, an optimal window of time for differentiating between tissue types is determined by identifying at least one bin in which the measure of difference between the two tissue types is substantially maximized. For example, an optimal window of time may be chosen to include every time bin in which a respective classification model provides an accuracy of 70% or greater. Here, the optimal window describes a period of time following application of a contrast agent in which an optical signal can be obtained for purposes of classifying the state of health of the tissue sample with an accuracy of at least 70%. Models distinguishing between three or more categories of tissue may also be used in determining an optimal window for obtaining spectral data. As discussed below, other factors may also be considered in determining the optimal window.

[0392] An analogous embodiment includes determining an optimal threshold or range of a measure of change of an optical signal to use in obtaining (or triggering the acquisition of) the same or a different signal for predicting the state of health of the sample. Instead of determining a specific, fixed window of time, this embodiment includes determining an optimal threshold of change in a signal, such as a video image whiteness intensity signal, after which an optical signal, such as a diffuse reflectance spectrum and/or a fluorescence spectrum, can be obtained to accurately characterize the state of health or other characteristic of the sample. This illustrative embodiment includes monitoring reflectance and/or fluorescence at a single or multiple wavelength(s), and upon reaching a threshold change from the initial condition, obtaining a full reflectance and/or fluorescence spectrum for use in diagnosing the region of tissue. This method allows for reduced data retrieval and monitoring, since it involves continuous tracking of a single, partial-spectrum or discrete-wavelength “trigger” signal (instead of multiple, full-spectrum scans), followed by the acquisition of spectral data in a spectral scan for use in tissue characterization, for example, the tissue classification module 132 of FIG. 1. Alternatively, the trigger may include more than one discrete-wavelength or partial-spectrum signal. The measure of change used to trigger obtaining one or more optical signals for tissue classification may be a weighted measure, and/or it may be a combination of measures of change of more than one signal.

[0393] In a further illustrative embodiment, instead of determining an optimal threshold or range of a measure of change of an optical signal, an optimal threshold or range of a measure of the rate of change of an optical signal is determined. For example, the rate of change of reflectance and/or fluorescence is monitored at a single or multiple wavelength(s), and upon reaching a threshold rate of change, a spectral scan is performed to provide spectral data for use in diagnosing the region of tissue. The measure of rate of change used to trigger obtaining one or more optical signals for tissue classification may be a weighted measure, and/or it may be a combination of measures of change of more than one signal. For example, the measured rate of change may be weighted by an initial signal intensity.

[0394] According to the illustrative embodiment, the optimum time window includes a time window in which spectra from cervical tissue may be obtained such that sites indicative of grades 2 and 3 cervical intraepithelial neoplasia (CIN ⅔) can be separated from non-CIN ⅔ sites. Non-CIN ⅔ sites include sites with grade 1 cervical intraepithelial neoplasia (CIN 1), as well as NED sites, normal columnar and normal squamous epithelia, and mature and immature metaplasia. Alternately, sites indicative of high grade disease, CIN 2+, which includes CIN ⅔ categories, carcinoma in situ (CIS), and cancer, may be separated from non-high-grade-disease sites. In general, for any embodiment discussed herein in which CIN ⅔ is used as a category for classification or characterization of tissue, the more expansive category CIN 2+ may be used alternatively. Preferably, the system 100 can differentiate amongst three or more classification categories. Exemplary embodiments are described below and include analysis of the time response of diffuse reflectance and/or 337-nm fluorescence spectra of a set of reference tissue samples with regions having known states of health to determine temporal characteristics indicative of the respective states of health. These characteristics are then used in building a model to determine a state of health of an unknown tissue sample. Other illustrative embodiments include analysis of fluorescence spectra using other excitation wavelengths, such as 380 nm and 460 rn, for example.

[0395] According to one illustrative embodiment, an optimum window is determined by tracking the difference between spectral data of two tissue types using a discrimination function.

[0396] According to the illustrative embodiment, the discrimination function shown below in Equation 76 may be used to extract differences between tissue types:

[0397] $\begin{array}{cc}D\left(\lambda \right)=\frac{{\mu \left(\mathrm{test}\left(\lambda \right)\right)}_{\mathrm{non}\text{-}\mathrm{CIN}2/3}-{\mu \left(\mathrm{test}\left(\lambda \right)\right)}_{\mathrm{CIN}2/3}}{\sqrt{{{\sigma }^2\left(\mathrm{test}\left(\lambda \right)\right)}_{\mathrm{non}\text{-}\mathrm{CIN}2/3}+{{\sigma }^2\left(\mathrm{test}\left(\lambda \right)\right)}_{\mathrm{CIN}2/3}}}& \left(76\right)\end{array}$
where μ corresponds to the mean optical signal for the tissue type indicated in the subscript; and a corresponds to the standard deviation. The categories CIN ⅔ and non-CIN ⅔ are used in this embodiment because spectral data is particularly well-suited for differentiating between these two categories of tissue, and because spectral data is prominently used in one embodiment of the classification schema in the tissue classification module in step 132 of FIG. 1 to identify CIN ⅔ tissue. Thus, in this way, it is possible to tailor the choice of an optimal scan window such that spectral data obtained within that window are well-adapted for use in identifying CIN ⅔ tissue in the tissue classification scheme 132. In one illustrative embodiment, the optical signal in Equation 76 includes diffuse reflectance. In another illustrative embodiment, the optical signal includes 337-nm fluorescence emission spectra. Other illustrative embodiments use fluorescence emission spectra at another excitation wavelength such as 380 nm and 460 nm. In still other illustrative embodiments, the optical signal is a video signal, Raman signal, or infrared signal. Some illustrative embodiments include using difference spectra calculated between different phases of acetowhitening, using various normalization schema, and/or using various combinations of spectral data and/or image data as discussed above.

[0398] In one preferred embodiment, determining an optimal window for obtaining spectral data in step 104 of FIG. 1 includes developing linear discriminant analysis models using spectra from each time bin shown in Table 1 below.

[0399]

TABLE 1
Time bins for which means spectra are obtained
in an exemplary embodiment
	Bin	Time after application of Acetic Acid (s)
	1	t ≦ 0
	2	0 < t ≦ 40
	3	40 < t ≦ 60
	4	60 < t ≦ 80
	5	80 < t ≦ 100
	6	100 < t ≦ 120
	7	120 < t ≦ 140
	8	140 < t ≦ 160
	9	160 < t ≦ 180
	10	t > 180

[0400] Alternatively, nonlinear discriminant analysis models may be developed. Generally, models for the determination of an optimal window are trained using reflectance and fluorescence data separately, although some embodiments include using both data types to train a model. The discriminant analysis models discussed herein for exemplary embodiments of the determination of an optimal window are generally less sophisticated than the schema used in the tissue classification module 132 in FIG. 1. Alternatively, a model based on the tissue classification schema in the module 132 in FIG. 1 can be used to determine an optimal window for obtaining spectral data in step 104 of FIG. 1.

[0401] In exemplary embodiments for determining an optimal window discussed herein, reflectance and fluoroescence intensities are down-sampled to one value every 10 nm between 360 and 720 nm. A model is trained by adding and removing intensities in a forward manner, continuously repeating the process until the model converges such that additional intensities do not appreciably improve tissue classification. Testing is performed by a leave-one-spectrum-out jack-knife process.

[0402] FIG. 35 shows the difference between the mean reflectance spectra for non-CIN ⅔ tissues and CIN ⅔ tissues at three times (prior to the application of acetic acid (graph 976), maximum whitening (graph 978, about 60-80 seconds post-AA), and the last time data were obtained (graph 980, about 160-180 seconds post-AA)). The time corresponding to maximum whitening was determined from reflectance data, and occurs between about 60 seconds and 80 seconds following application of acetic acid. In the absence of acetic acid, the reflectance spectra for CIN ⅔ (curve 982 of graph 976 in FIG. 35) are on average lower than non-CIN ⅔ tissue (curve 984 of graph 976 in FIG. 35). Following the application of acetic acid, a reversal is noted—CIN ⅔ tissues have higher reflectance than the non-CIN ⅔ tissues. The reflectance of CfN ⅔ and non-CIN ⅔ tissues increase with acetic acid, with CIN ⅔ showing a larger relative percent change (compare curves 986 and 988 of graph 978 in FIG. 35). From about 160 s to about 180 s following acetic acid, the reflectance of CIN ⅔ tissue begins to return to the pre-acetic acid state, while the reflectance of the non-CIN ⅔ group continues to increase (compare curves 990 and 992 of graph 980 in FIG. 35)

[0403] Discrimination function ‘spectra’ are calculated from the reflectance spectra of CIN ⅔ and non-CIN ⅔ tissues shown in FIG. 35 as one way to determine an optimal window for obtaining spectral data. Discrimination function spectra comprise values of the discrimination function in Equation 76 determined as a function of wavelength for sets of spectral data obtained at various times. As shown in FIG. 36, the largest differences (measured by the largest absolute values of discrimination function) are found about 60s to about 80s post-acetic acid (curve 1002), and these data agree with the differences seen in the mean reflectance spectra of FIG. 35 (curves 986 and 988 of graph 978 in FIG. 35).

[0404] Multivariate linear regression analysis takes into account wavelength interdependencies in determining an optimal data acquisition window. One way to do this is to classify spectral data shown in FIG. 35 using a model developed from the reflectance data for each of the bins in Table 1. Then, the accuracy of the models for each bin is computed and compared. Reflectance intensities are down-sampled to one about every 10 nm between about 360 nm and about 720 nm. The model is trained by adding intensities in a forward-stepped manner. Testing is performed with a leave-one-spectrum-out jack-knife process. The results of the linear regression show which wavelengths best separate CIN ⅔ from non-CIN ⅔, as shown in Table 2.

[0405]

TABLE 2
Forwarded selected best reflectance wavelengths
for classifying CIN 2/3 from non-CIN 2/3 spectra obtained
at different times pre and post-AA.
Time from AA (s)	LDA Model Input Wavelengths (nm)	Accuracy
−30	370 400 420 440 530 570 590 610	66
30	420 430 450 600	74
50	360 400 420 430 580 600	74
70	360 370 420 430 560 580 600	77
90	360 420 430 540 590	73
110	360 440 530 540 590	71
130	360 420 430 540 590	71
150	370 400 430 440 540 620 660 690 720	72
170	490 530 570 630 650	75

[0406] As shown in Table 2, the two best models for separating CIN ⅔ and non-CIN ⅔, taking into account wavelength interdependence, use reflectance data obtained at peak CIN ⅔ whitening (from about 60s to about 80s) and reflectance data obtained from about 160s to about 180s post acetic acid. The first model uses input wavelengths between about 360 and about 600 nm, while the second model uses more red-shifted wavelengths between about 490 and about 650 nm. This analysis shows that the optimal windows are about 60s-80s post AA and about 160-180 post AA (the latest time bin). This is consistent with the behavior of the discrimination function spectra shown in FIG. 6.

[0407] FIG. 37 demonstrates one step in determining an optimal window for obtaining spectral data, for purposes of discriminating between CIN ⅔ and non-CIN ⅔ tissue. FIG. 37 shows a graph 1006 depicting the performance of the two LDA models described in Table 2 above as applied to reflectance spectral data obtained at various times following application of acetic acid 1008. Curve 1010 in FIG. 37 is a plot of the diagnostic accuracy of the LDA model based on reflectance spectral data obtained between about 60 and about 80 seconds (“peak whitening model”) as applied to reflectance spectra from the bins of Table 1, and curve 1012 in FIG. 37 is a plot of the diagnostic accuracy of the LDA model based on reflectance spectral data obtained between about 160 and about 180 seconds, as applied to reflectance spectra from the bins of Table 1. For the peak-whitening model, the highest accuracy was obtained at about 70s, while accuracies greater than 70% were obtained with spectra collected in a window between about 30s and about 130s. The 160-180s model had a narrower window around 70s, but performs better at longer times.

[0408] FIG. 38 shows the difference between the mean 337-nm fluorescence spectra for non-CIN ⅔ tissues and CIN ⅔ tissues at three times (prior to application of acetic acid (graph 1014), maximum whitening (graph 1016, about 60 to about 80 seconds post-AA), and at a time corresponding to the latest time period in which data was obtained (graph 1018, about 160 to about 180 seconds post-AA)). The time corresponding to maximum whitening was determined from reflectance data, and occurs between about 60 seconds and 80 seconds following application of acetic acid. In the absence of acetic acid, the fluorescence spectra for CIN ⅔ tissue (curve 1020 of graph 1014 in FIG. 38) and for non-CIN ⅔ tissue (curve 1022 of graph 1014 in FIG. 38) are essentially equivalent with a slightly lower fluorescence noted around 390 nm for CIN ⅔ sites. Following the application of acetic acid, the fluorescence of CIN ⅔ and non-CIN ⅔ tissues decrease, with CIN ⅔ showing a larger relative percent change (compare curves 1024 and 1026 of graph 1016 in FIG. 38). From about 160s to about 180 s following acetic acid application, the fluorescence of CIN ⅔ tissue shows signs of returning to the pre-acetic acid state while the fluorescence of the non-CIN ⅔ group continues to decrease (compare curves 1028 and 1030 of graph 1018 in FIG. 38).

[0409] An optimal data acquisition window may also be obtained using a discrimination function calculated from fluorescence spectra of CIN ⅔ and non-CIN ⅔ tissues shown in FIG. 38. In one example, discrimination function spectra include values of the discrimination function in Equation 76 determined as a function of wavelength for sets of spectral data obtained at various times. FIG. 39 shows a graph 1032 depicting the discrimination function spectra evaluated using the fluorescence data of FIG. 38 obtained prior to application of acetic acid, and at two times post-AA. As shown in FIG. 39, applications of acetic acid improves that distinction between CIN ⅔ and non-CIN ⅔ tissues using fluorescence data. The largest absolute values are found using data measured within the range of about 160-180s post-acetic acid (curve 1042), and these agree with the differences seen in the mean fluorescence spectra of FIG. 38 (curves 1030 and 1028 of graph 1018 in FIG. 38).

[0410] Multivariate linear regression takes into account wavelength interdependencies in determining an optimal data acquisition window. An application of one method of determining an optimal window includes classifying data represented in the CIN ⅔, CIN 1, and NED categories in the Appendix Table into CIN ⅔ and non-CIN ⅔ categories by using classification models developed from the fluorescence data shown in FIG. 38. Fluoroescence intensities are down-sampled to one about every 10 nm between about 360 and about 720 nm. The model is trained by adding intensities in a forward manner. Testing is performed by a leave-one-spectrum-out jack-knife process. The result of this analysis shows which wavelengths best separate CIN ⅔ from non-CIN ⅔, as shown in Table 3.

[0411]

TABLE 3
Forwarded selected best 337-nm fluorescence wavelengths
for classifying CIN 2/3 from non-CIN 2/3 spectra obtained at
different times pre and post-AA.
Time from AA (s)	LDA Model Input Wavelengths (nm)	Accuracy
−30	380, 430, 440, 610, 660, 700, 710	61
30	370, 380, 390, 640	61
50	410	54
70	360, 390, 490, 580, 590, 670	63
90	370, 380, 420, 460, 500, 560, 660	64
110	360, 390, 400, 710	51
130	370	53
150	370, 380, 440, 620, 640, 700	65
170	370, 480, 510, 570, 600, 700, 720	76

[0412] As shown in Table 3, the two best models for separating CIN ⅔ and non-CIN ⅔, taking into account wavelength interdependencies, use. data obtained at peak CIN ⅔ whitening (60-80s) and data obtained at the latest time measured (from about 160s to about 180s post acetic acid). The first model uses input wavelengths between about 360 and about 670 nm, while the second model uses wavelengths between about 370 and about 720 nm.

[0413] FIG. 40 demonstrates one step in determining an optimal window. FIG. 40 shows a graph 1044 depicting the performance of the two LDA models described in Table 3 above as applied to fluorescence spectral data obtained at various times following application of acetic acid 1046. Curve 1048 in FIG. 40 is a plot of the diagnostic accuracy of the LDA model based on fluorescence spectral data obtained between about 60 and about 80 seconds (“peak whitening model”) as applied to fluorescence spectra from the bins of Table 1, and curve 1050 in FIG. 40 is a plot of the diagnostic accuracy of the LDA model based on fluorescence spectral data obtained between about 160 and about 180 seconds, as applied to fluorescence spectra from the bins of Table 1. The accuracies of these models vary depending on when the fluorescence spectra are recorded relative to the application of acetic acid, as shown in FIG. 40. The predictive ability of the fluorescence models in FIG. 40 tend to be less than that of the reflectance models in FIG. 37. Accuracies greater than 70% are obtained with spectra collected after about 160 seconds post-AA.

[0414] One embodiment includes classifying spectral data shown in FIG. 38 from known reference tissue samples into CIN ⅔ and non-CIN ⅔ categories by using classification models developed from the fluorescence data for each of the bins in Table 1. Models are developed based on time post acetic acid. Ratios of fluorescence to reflectance are down-sampled to one every 10 nm between about 360 and about 720 nm. The model is trained by adding intensities in a forward manner. Testing is performed by a leave-one-spectrum-out jack-knife process. For this analysis, the model is based on intensities at about 360, 400, 420, 430, 560, 610, and 630 nm. In general, the results are slightly better than a model based on fluorescence alone. Improved performance is noted from spectra acquired at about 160s post acetic acid.

[0415] FIG. 41 shows a graph 1052 depicting the accuracy of three LDA models as applied to spectral data obtained at various times following application of acetic acid 1054, used in determining an optimal window for obtaining spectral data. Curve 1056 in FIG. 41 is a plot of the diagnostic accuracy of the LDA model based on reflectance spectral data obtained between about 60 and about 80 seconds (“peak whitening model”), also shown as curve 1010 in FIG. 37. Curve 1058 in FIG. 41 is a plot of the diagnostic accuracy of the LDA model based on fluorescence spectral data obtained between about 60 and about 80 seconds (“peak whitening model”), also shown as curve 1048 in FIG. 40. Curve 1060 in FIG. 41 is a plot of the diagnostic accuracy of the LDA model based on fluorescence intensity divided by reflectance, as described in the immediately preceding paragraph.

[0416] The exemplary embodiments discussed above and illustrated in FIGS. 35 to 41 provide a basis for selecting an optimum window for obtaining spectral data upon application of acetic acid. Other factors to be considered include the time required to apply the contrast agent and to perform target focusing as shown in FIG. 27A. Another factor is the time required to perform a scan over a sufficient number of regions of a tissue sample to provide an adequate indication of disease state with sufficient sensitivity and selectivity. Also, a consideration may be made for the likelihood of the need for and time required for retakes due to patient motion.

[0417] The factors and analysis discussed above indicate that an optimal data acquisition window is a period of time from about 30 seconds following application of a contrast agent (for example, a 5 volume percent acetic acid solution) to about 130 seconds following application of the contrast agent. Other optimal windows are possible. For example, one alternative embodiment uses an optimal window with a “start” time from about 10 to about 60 seconds following application of acetic acid, and an “end” time from about 110 to about 180 seconds following application of acetic acid.

[0418] An alternative manner for determining an optimal window comprises determining and using a relative amplitude change and/or rate of amplitude change as a trigger for obtaining spectral data from a sample. By using statistical and/or heuristic methods such as those discussed herein, it is possible to relate more easily-monitored relative changes or rates-of-change of one or more optical signals from a tissue sample to corresponding full spectrum signals that can be used in characterizing the state of health of a given sample. For example, by performing a discrimination function analysis, it may be found for a given tissue type that when the relative change in reflectance at a particular wavelength exceeds a threshold value, the corresponding full-spectrum reflectance can be obtained and then used to accurately classify the state of health of the tissue. In addition, the triggers determined above may be converted into optimal time windows for obtaining diagnostic optical data from a sample.

[0419] FIG. 42 shows how an optical amplitude trigger is used to determine an optimal time window for obtaining diagnostic optical data. The graph 1062 in FIG. 42 plots the normalized relative change of mean reflectance signal 1064 from tissue samples with a given state of health as a function of time following application of acetic acid 1066. The mean reflectance signal determined from CIN 1, CIN 2, and Metaplasia samples are depicted in FIG. 42 by curves 1068, 1070, and 1072, respectively. FIG. 42 shows that when the normalized relative change of mean reflectance reaches or exceeds 0.75 in this example, the image intensity data and/or the fuill reflectance and/or fluorescence spectrum is most indicative of a given state of health of a sample. Thus, for CIN 2 samples, for example, this corresponds to a time period between t₁ and t₂, as shown in the graph 1062 of FIG. 42. Therefore, spectral and/or image data obtained from a tissue sample between t₁ and t₂ following application of acetic acid are used in accurately determining whether or not CIN 2 is indicated for that sample. In one embodiment, the relative change of reflectance of a tissue sample at one or more given wavelengths is monitored. When that relative change is greater than or equal to the 0.75 threshold, for example, more comprehensive spectral and/or image data are obtained to characterize whether the sample is indicative of CIN 2. In another embodiment, a predetermined range of values of the relative optical signal change is used such that when the relative signal change falls within the predetermined range of values, additional spectral and/or image data is captured in order to characterize the sample.

[0420] FIG. 43 shows how a rate-of-change of an optical amplitude trigger is used to determine an optimal time window for obtaining diagnostic optical data. The graph 1074 of FIG. 43 plots the slope of an exemplary mean reflectance signal 1076 from tissue samples with a given state of health as a function of time following application of acetic acid 1078. The slope of mean reflectance is a measure of the rate of change of the mean reflectance signal. The rate of change of mean reflectance determined from CIN 1, CIN 2, and metaplasia samples are depicted in FIG. 43 by curves 1080, 1082, and 1084, respectively. Those curves show that when the absolute value of the slope is less than or equal to 0.1, for example, in the vicinity of maximum reflectance, the image intensity data and/or the full reflectance and/or fluorescence spectrum is most indicative of a given state of health of a sample. Thus, for CIN 2 samples, for example, this corresponds to a time period between t₁ and t₂ as shown in the graph 1074 of FIG. 43. Therefore, spectral and/or image data obtained from a tissue sample between t₁ and t₂ following application of acetic acid is used in accurately determining whether or not CIN 2 is indicated for that sample. In the example, the rate of change of reflectance of a tissue sample is monitored at one or more wavelengths. When that rate of change has an absolute value less than or equal to 0.1, more comprehensive spectral and/or image data are obtained from the sample for purposes of characterizing whether or not the sample is indicative of CIN 2. FIG. 43 demonstrates use of a range of values of rate of optical signal change. Other embodiments use a single threshold value.

[0421] In one embodiment, the tissue characterization system shown in FIG. 1 comprises real-time motion tracking (step 106 in FIG. 1). Real-time tracking determines a correction for and/or compensates for a misalignment between two images of the tissue sample obtained during a spectral data scan (i.e. step 732 in FIG. 27A and 27B), where the misalignment is caused by a shift in the position of the sample with respect to the instrument 102 in FIG. 1 (or, more particularly, the probe optics 178). The misalignment may be caused by unavoidable patient motion, such as motion due to breathing during the spectral data scan 732.

[0422] In one embodiment, the correction factor determined by the real-time tracker is used to automatically compensate for patient motion, for example, by adjusting the instrument 102 (FIG. 1) so that spectral data obtained from indexed regions of the tissue sample during the scan correspond to their originally-indexed locations. Alternatively or additionally, the motion correction factor can be used in spectral data pre-processing, step 114 in FIG. 1 and FIG. 11, to correct spectral data obtained during a scan according to an applicable correction factor. For example, the spectral data lookup method in step 114 of FIG. 1 as discussed herein may compensate for patient motion by using a correction determined by the real-time tracker 106 to correlate a set of lo spectral data obtained during a scan with its true, motion-corrected position (x,y) on the tissue sample. In one embodiment, the motion correction factor determined in step 106 of FIG. 1 is updated about once every second during the scan using successive images of the tissue, as shown in FIG. 27B. Step 106 determines and validates a motion correction factor about once every second during the spectral scan, corresponding to each successive image in FIG. 27B. Then, the pre-processing component 114 of FIG. 1 corrects the spectral data obtained at an interrogation point during the spectral scan using the correction factor corresponding to the time at which the spectral data were obtained.

[0423] A typical misalignment between two images obtained about 1 second apart is less than about 0.55-mm within a two-dimensional, 480×500 pixel image frame field covering a tissue area of approximately 25-mm×25-mm. These dimensions provide an example of the relative scale of misalignment versus image size. In some instances it is only necessary to compensate for misalignments of less than about one millimeter within the exemplary image frame field defined above. In other cases, it is necessary to compensate for misalignments of less than about 0.3-mm within the exemplary image frame field above. Also, the dimensions represented by the image frame field, the number of pixels of the image frame field, and/or the pixel resolution may differ from the values shown above.

[0424] A misalignment correction determination may be inaccurate, for example, due to any one or a combination of the following: non-translational sample motion such as rotational motion, local deformation, and/or warping; changing features of a sample such as whitening of tissue; and image recording problems such as focus adjustment, missing images, blurred or distorted images, low signal-to-noise ratio, and computational artifacts. Validation procedures of the invention identify such inaccuracies. The methods of validation may be conducted “on-the-fly” in concert with the methods of determining misalignment corrections in order to improve accuracy and to reduce the time required to conduct a given test.

[0425] In order to facilitate the automatic analysis in the tissue classification system 100 of FIG. 1, it is often necessary to adjust for misalignments caused by tissue sample movement that occurs during the diagnostic procedure. For example, during a given procedure, in vivo tissue may spatially shift within the image frame field from one image to the next due to movement of the patient. Accurate tissue characterization requires that this movement be taken into account in the automated analysis of the tissue sample. In one embodiment, spatial shift correction made throughout a spectral data scan is more accurate than a correction made after the scan is complete, since “on-the-fly” corrections compensate for smaller shifts occurring over shorter periods of time and since spectral data is being continuously obtained throughout the approximately 12 to 15 second scan in the embodiment of FIG. 27B.

[0426] If a sample moves while a sequence of images is obtained, the procedure may have to be repeated. For example, this may be because the shift between consecutive images is too large to be accurately compensated for, or because a region of interest moves outside of a usable portion of the frame captured by the optical signal detection device. Stepwise motion correction of spectral data reduces the cumulative effect of sample movement. If correction is made only after an entire sequence is obtained, it may not be possible to accurately compensate for some types of sample movement. On-the-fly, stepwise compensation for misalignment reduces the need for retakes.

[0427] On-the-fly compensation may also obviate the need to obtain an entire sequence of images before making the decision to abort a failed procedure, particularly when coupled with on-the-fly, stepwise validation of the misalignment correction determination. For example, if the validation procedure detects that a misalignment correction determination is either too large for adequate compensation to be made or is invalid, the procedure may be aborted before obtaining the entire sequence of images. It can be immediately determined whether or not the obtained data is useable. Retakes may be performed during the same patient visit; no follow-up visit to repeat an erroneous test is required. A diagnostic test invalidated by excessive movement of the patient may be aborted before obtaining the entire sequence of images, and a new scan may be completed, as long as there is enough remaining time in the optimal time window for obtaining spectral data.

[0428] In preferred embodiments, a determination of misalignment correction is expressed as a translational displacement in two dimensions, x and y. Here, x and y represent Cartesian coordinates indicating displacement on the image frame field plane. In other embodiments, corrections for misalignment are expressed in terms of non-Cartesian coordinate systems, such as biradical, spherical, and cylindrical coordinate systems, among others. Alternatives to Cartesian-coordinate systems may be useful, for example, where the image frame field is non-planar.

[0429] Some types of sample motion—including rotational motion, warping, and local deformation—may result in an invalid misalignment correction determination, since it may be impossible to express certain instances of these types of sample motion in terms of a translational displacement, for example, in the two Cartesian coordinates x and y. It is noted, however, that in some embodiments, rotational motion, warping, local deformation, and/or other kinds of non-translational motion are acceptably accounted for by a correction expressed in terms of a translational displacement. The changing features of the tissue, as in acetowhitening, may also affect the determination of a misalignment correction. Image recording problems such as focus adjustment, missing images, blurred or distorted images, low signal-to-noise ratio (i.e. caused by glare), and computational artifacts may affect the correction determination as well. Therefore, validation of a determined correction is often required. In some embodiments, a validation step includes determining whether an individual correction for misalignment is erroneous, as well as determining whether to abort or continue the test in progress. Generally, validation comprises splitting at least a portion of each of a pair of images into smaller, corresponding units (subimages), determining for each of these smaller units a measure of the displacement that occurs within the unit between the two images, and comparing the unit displacements to the overall displacement between the two images.

[0430] In certain embodiments, the method of validation takes into account the fact that features of a tissue sample may change during the capture of a sequence of images. For example, the optical intensity of certain regions of tissue change during the approximately 12 to 15 seconds of a scan, due to acetowhitening of the tissue. Therefore, in one embodiment, validation of a misalignment correction determination is performed using a pair of consecutive images. In this way, the difference between the corresponding validation cells of the two consecutive images is less affected by gradual tissue whitening changes, as compared with images obtained further apart in time. In an alternative embodiment, validation is performed using pairs of nonconsecutive images taken within a relatively short period of time, compared with the time in which the overall sequence of images is obtained. In other embodiments, validation comprises the use of any two images in the sequence of images.

[0431] A determination of misalignment correction between two images is inadequate if significant portions of the images are featureless or have low signal-to-noise ratio (i.e. are affected by glare). Similarly, validation using cells containing significant portions that are featureless or that have low signal-to-noise ratio may result in the erroneous invalidation of valid misalignment correction determinations. This may occur in cases where the featureless portion of the overall image is small enough so that it does not adversely affect the misalignment correction determination. For example, analysis of featureless validation cells may produce meaningless correlation coefficients. One embodiment includes identifying one or more featureless cells and eliminating them from consideration in the validation of a misalignment correction determination, thereby preventing rejection of a good misalignment correction.

[0432] A determination of misalignment. correction may be erroneous due to a computational artifact of data filtering at the image borders. For example, in one exemplary embodiment, an image with large intensity differences between the upper and lower borders and/or the left and right borders of the image frame field undergoes Laplacian of Gaussian frequency domain filtering. Since Laplacian of Gaussian frequency domain filtering corresponds to cyclic convolution in the space-time domain, these intensity differences (discontinuities) yield a large gradient value at the image border, and cause the overall misalignment correction determination to be erroneous, since changes between the two images due to spatial shift are dwarfed by the edge effects. One alternative embodiment employs pre-multiplication of image data by a Hamming window to remove or reduce this “wraparound error.” However, one preferred embodiment employs an image-blending technique such as feathering, to smooth any border discontinuity, while requiring only a minimal amount of additional processing time.

[0433] FIG. 44A represents a 480×500 pixel image 1086 from a sequence of images of in vivo human cervix tissue and shows a 256×256 pixel portion 1088 of the image that the motion correction step 106 in FIG. 1 uses in identifying a misalignment correction between two images from a sequence of images of the tissue, according to one embodiment. The image 1086 of FIG. 44A has a pixel resolution of about 0.054-mm. The embodiments described herein show images with pixel resolutions of about 0.0547-mm to about 0.0537-mm. Other embodiments have pixel resolutions outside this range. In some embodiments, the images of a sequence have an average pixel resolution of between about 0.044-mm and about 0.064-mm. In the embodiment of FIG. 44A, step 106 of the system of FIG. 1 uses the central 256×256 pixels 1088 of the image 1086 for motion tracking. An alternative embodiment uses a region of different size for motion tracking, which may or may not be located in the center of the image frame field. In the embodiment of FIG. 44A, the motion tracking step 106 of FIG. 1 determines an x-displacement and a y-displacement corresponding to the translational shift (misalignment) between the 256×256 central portions 1088 of two images in the sequence of images obtained during a patient spectral scan.

[0434] The determination of misalignment correction may be erroneous for any number of various reasons, including but not limited to non-translational sample motion (i.e. rotational motion, local deformation, and/or warping), changing features of a sample (i.e. whitening of tissue), and image recording problems such as focus adjustment, missing images, blurred or distorted images, low signal-to-noise ratio, and computational artifacts. Therefore, in preferred embodiments, validation comprises splitting an image into smaller units (called cells), determining displacements of these cells, and comparing the cell displacements to the overall displacement. FIG. 44B depicts the image represented in FIG. 44A and shows a 128×128 pixel portion 1090 of the image, made up of 16 individual 32×32 pixel validation cells 1092, from which data is used to validate the misalignment correction.

[0435] FIG. 45, FIGS. 46A and B, and FIGS. 47A and B depict steps in illustrative embodiment methods of determining a misalignment correction between two images of a sequence, and methods of validating that determination. Steps 1096 and 1098 of FIG. 45 show development of data from an initial image with which data from a subsequent image are compared in order to determine a misalignment correction between the subsequent image and the initial image. An initial image “o” is preprocessed, then filtered to obtain a matrix of values, for example, optical luminance (brightness, intensity), representing a portion of the initial image. In one embodiment, preprocessing comprises transforming the three RGB color components corresponding to a given pixel into a single luminance value. An exemplary luminance is CCIR 601, shown in Equation 63. CCIR 601 luminance may be used, for example, as a measure of the “whiteness” of a particular pixel in an image from an acetowhitening test. Different expressions for grayscale luminance may be used, and the choice may be geared to the specific type of diagnostic test conducted. The details of step 1096 of FIG. 45 is illustrated in blocks 1130, 1132, and 1134 of FIG. 46A, where block 1130 represents the initial color image, “o”, in the sequence, block 1132 represents conversion of color data to grayscale using Equation 63, and block 1134 represents the image of block 240 after conversion to grayscale. Referring now to FIGS. 46A and 46B, FIG. 46B is a continuation of FIG. 46A, linked, for example, by the circled connectors labeled A and B. Accordingly, going forward, FIGS. 46A and 46B are referred to as FIG. 46.

[0436] Step 1098 of FIG. 45 represents filtering a 256×256 portion of the initial image, for example, a portion analogous to the 256×256 central portion 1088 of the image 1086 of FIG. 44A, using Laplacian of Gaussian filtering. Other filtering techniques are used in other embodiments. Preferred embodiments employ Laplacian of Gaussian filtering, which combines the Laplacian second derivative approximation with the Gaussian smoothing filter to reduce the high frequency noise components prior to differentiation. This filtering step may be performed by discrete convolution in the space domain, or by frequency domain filtering. The Laplacian of Gaussian (LoG) filter may be expressed in terms of x and y coordinates (centered on zero) as shown in Equation 77:

[0437] $\begin{array}{cc}\mathrm{LoG}\left(x,y\right)=-\frac{1}{\pi {\sigma }^4}\left[1-\frac{x^2+y^2}{2{\sigma }^2}\right]e^{-\frac{x^2+y^2}{2{\sigma }^2}}& \left(77\right)\end{array}$
where x and y are space coordinates and (y is the Gaussian standard deviation. In one preferred embodiment, an approximation to the LoG fuinction is used. Illustrative embodiments described herein include use of an approximation kernel(s) of size 9×9, 21×21, and/or 31×31. The Gaussian standard deviation, σ, is chosen in certain preferred embodiments using Equation 78:
σ=LoG filter size/8.49  (78)
where LoG filter size corresponds to the size of the discrete kernel approximation to the LoG function (i.e. 9, 21, and 31 for the approximation kernels used herein). Other embodiments employ different kernel approximations and/or different values of Gaussian standard deviation.

[0438] The LoG filter size may be chosen so that invalid scans are failed and valid scans are passed with a minimum of error. Generally, use of a larger filter size is better at reducing large structured noise and is more sensitive to larger image features and larger motion, while use of a smaller filter size is more sensitive to smaller features and smaller motion. One embodiment of the invention comprises adjusting filter size to coordinate with the kind of motion being tracked and the features being imaged.

[0439] The details of step 1098 of FIG. 45 is illustrated in FIG. 46 in blocks 1134, 1136, and 1138 where block 1134 represents data from the initial image in the sequence after conversion to grayscale luminance, block 1136 represents the application of the LoG filter, and block 1138 represents the 256×256 matrix of data values, G_o(x,y), which is the “gold standard” by which other images are compared in validating misalignment correction determinations in this embodiment. As detailed in FIGS. 47A and 47B, one embodiment validates a misalignment correction determination by comparing a given image to its preceding image in the sequence, not by comparing a given image to the initial image in the sequence as shown in FIG. 46. (Referring now to FIGS. 47A and 47B, FIG. 47B is a continuation of FIG. 47A, linked, for example, by the circled connectors labeled A, B, and C. Accordingly, going forward, FIGS. 47A and 47B are referred to as FIG. 47.) Although FIG. 45, FIG. 46, and FIG. 47 show application of the LoG filter as a discrete convolution in the space domain, resulting in a standard expressed in space coordinates, other embodiments comprise applying the LoG filter in the frequency domain. In either case, the LoG filter is preferably zero padded to the image size.

[0440] The details of steps 1100 and 1102 of FIG. 45 represent preprocessing an image “i” by converting RGB values to grayscale luminance as discussed above, and performing LoG filtering to obtain G_i(x,y), a matrix of values from image “i” which is compared with that of another image in the sequence in order to determine a misalignment correction between the two images. The details of steps 1100 and 1102 of FIG. 45 are illustrated in FIG. 46 in blocks 1140, 1142, 1144, 1146, and 1148, where f_i(x,y) in block 1140 is the raw image data from image “i”, block 1142 represents conversion of the f_i(x,y) data to gray scale intensities as shown in block 1144, and block 1146 represents application of the LoG filter on the data of block 1144 to produce the data of block 1148, G_i(x,y).

[0441] Similarly, steps 1106 and 1108 of FIG. 45 represent preprocessing an image “j” by converting RGB values to grayscale luminance as discussed above, and performing LoG filtering to obtain G_j(x,y), a matrix of values from image “j” which is compared with image “i” in order to determine a measure of misalignment between the two images. In some preferred embodiments, image “j” is subsequent to image “i” in the sequence. In some preferred embodiments, “i” and “j” are consecutive images. Steps 1106 and 1108 of FIG. 45 are illustrated in FIG. 46 in blocks 1154, 1156, 1158, 1160, and 1162, where “j” is “i+1”, the image consecutive to image “i” in the sequence. In FIG. 46, block 1154 is the raw “i+1” image data, block 1156 represents conversion of the “i+1” data to gray scale intensities as shown in block 1158, and block 1160 represents application of the LoG filter on the data of block 1158 to produce the data of block 1162, G_i+1(x,y).

[0442] Steps 1104 and 1110 of FIG. 45 represent applying a Fourier transform, for example, a Fast Fourier Transform (FFT), using G_i(x,y) and G_j(x,y), respectively, to obtain F_i(u,v) and F_j(u,v), which are matrices of values in the frequency domain corresponding to data from images “i” and “j”, respectively. Details of steps 1104 and 1110 of FIG. 45 are illustrated in FIG. 46 by blocks 1148, 1150, 1152, 1162, 1164, and 1166, where “i” is “i+1”, the image consecutive to image “i” in the sequence. In FIG. 46, block 1148 represents the LoG filtered data, G_i(x,y), corresponding to image “i”, and block 1150 represents taking the Fast Fourier Transform of G_i(x,y) to obtain F_i(u,v), shown in block 1152. Similarly, in FIG. 46 block 1162 is the LoG filtered data, G_i+1(x,y), corresponding to image “i+1”, and block 1164 represents taking the Fast Fourier Transform of G_i+1(x,y) to obtain F_i+1(u,v), shown in block 1166.

[0443] Step 1112 of FIG. 45 represents computing the cross correlation F_i(u,v) F*_j(u,v), where F_i(u,v) is the Fourier transform of data from image “i”, F*_j(u,v) is the complex conjugate of the Fourier transform of data from image “j”, and u and v are frequency domain variables. The cross-correlation of two signals of length N₁ and N₂ provides N₁+N₂−1 values; thus avoiding aliasing problems due to under-sampling, the two signals should be padded with zeros up to N₁+N₂−1 samples. Details of step 1112 of FIG. 45 are represented in FIG. 46 by blocks 1152, 1166, and 1168. Block 1168 of FIG. 46 represents computing the cross correlation, F_i(u,v)F*_i+1(u,v), using F_i(u,v), the Fourier transform of data from image “i”, and F*_i+1(u,v), the complex conjugate of the Fourier transform of data from image “i+1”. The cross-correlation may also be expressed as c(k,l) in Equation 79:
c(k,l)=ΣΣI₁(p,q)I₂(p-k,q-1)  (79)
where variables (k,l) can be thought of as the shifts in each of the x- and y-directions which are being tested in a variety of combinations to determine the best measure of misalignment between two images I₁ and I₂, and where p and q are matrix element markers.

[0444] Step 1114 of FIG. 45 represents computing the inverse Fourier transform of the cross-correlation computed in step 1112. Step 1114 of FIG. 45 is represented in FIG. 46 by block 1170. The resulting inverse Fourier transform maps how well the 256×256 portions of images “i” and “j” match up with each other given various combinations of x- and y-shifts. Generally, the normalized correlation coefficient closest to 1.0 corresponds to the x-shift and y-shift position providing the best match, and is determined from the resulting inverse Fourier transform. In a preferred embodiment, correlation coefficients are normalized by dividing matrix values by a scalar computed as the product of the square root of the (0,0) value of the auto-correlation of each image. In this way, variations in overall brightness between the two images have a more limited effect on the correlation coefficient, so that the actual movement within the image frame field between the two images is better reflected in the misalignment determination.

[0445] Step 11116 of FIG. 45 represents deternining misalignment values d_x, d_y, d, sum(d_x), sum(d_y), and Sum(d_j), where d_x is the computed displacement between the two images “i” and “j” in the x-direction, d_y is the computed displacement between the two images in the y-direction, d is the square root of the sum d_x²+d_y² and represents an overall displacement between the two images, sum(d_x) is the cumulative x-displacement between the current image “j” and the first image in the sequence “o”, sum(d_y) is the cumulative y-displacement between the current image “j” and the first image in the sequence “o”, and Sum(d_j) is the cumulative displacement, d, between the current image “j” and the first image in the sequence “o”. Step 1116 of FIG. 45 is represented in FIG. 46 by blocks 1172, 1174, and 1176. Blocks 1174 and 1176 represent finding the maximum value in the data of block 1172 in order to calculate d_x, d_y, d, sum(d_x), sum(d_y), and Sum(d_i+1) as described above, where image “j” in FIG. 45 is “i+1” in FIG. 46, the image consecutive to image “i”. For example, in the scan illustrated by block 732 in FIG. 27B, if image “i” is the image at block 750, then image “j” is the next consecutive image (the image at block 752).

[0446] Steps 1118, 1120, and 1122 of FIG. 45 represent one method of validating the misalignment correction determined for image “j” in step 1116 of FIG. 45. This method of validating misalignment correction is represented in blocks 1177, 1179, 1181, 1190, 1192, and 1194 of FIG. 47. Another method of validating a misalignment correction is represented in steps 1124, 1126, and 1128 of FIG. 45; and this method is represented in blocks 1178, 1180, 1182, 1184, 1186, and 1188 of FIG. 46. FIG. 47 is a schematic flow diagram depicting steps in a version of the methods shown in FIG. 45 of determining a correction for a misalignment between two images in which validation is performed using data from two consecutive images. One embodiment includes using consecutive or near-consecutive images to validate a misalignment correction determination, as in FIG. 47. Other embodiments comprise using the initial image to validate a misalignment correction determination for a given image, as in FIG. 46.

[0447] In FIG. 45, step 1118 represents realigning G_j(x,y), the LoG-filtered data from image “j”, to match up with G_i(x,y), the LoG-filtered data from image “i”, using the misalignment values d_x and d_y determined in step 1116. In preferred embodiments, image “j” is consecutive to image “i” in the sequence of images. Here, image “j” is image “i+1” such that G_i(x,y) is aligned with G_i+1(x,y) as shown in block 1177 of FIG. 47. Similarly, in FIG. 45, step 1124 represents realigning G_j(x,y), the LoG-filtered data from image “j”, to match up with G_o(x,y), the LoG-filtered “gold standard” data from the initial image “o”, using the displacement values sum(d_x) and sum(d_y) determined in step 1116. Step 1124 of FIG. 45 is represented in block 1178 of FIG. 46.

[0448] Step 1120 of FIG. 45 represents comparing corresponding validation cells from G_j(x,y) and G_i(x,y) by computing correlation coefficients for each cell. This is represented schematically in FIG. 47 by blocks 1179, 1181, 1190, 1192, and 1194 for the case where j=i+1. First, a 128×128 pixel central portion of the realigned G_i+1(x,y) is selected, and the corresponding 128×128 pixel central portion of G_i(x,y) is selected, as shown in blocks 1179 and 1181 of FIG. 47. An exemplary 128×128 pixel validation region 1090 is shown in FIG. 44B. Then, one embodiment comprises computing a correlation coefficient for each of 16 validation cells. An exemplary validation cell from each of the realigned G_i+1(x,y) matrix 1181 and G_i(x,y) matrix 1179 is shown in blocks 1192 and 1190 of FIG. 47. The validation cells are as depicted in the 32×32 pixel divisions 1092 of the 128×128 pixel validation region 1090 of FIG. 44B. Different embodiments use different numbers and/or different sizes of validation cells. Correlation coefficients are computed for each of the 16 cells, as shown in block 1194 of FIG. 47. Each correlation coefficient is a normalized cross-correlation coefficient as shown in Equation 80:

[0449] $\begin{array}{cc}{c}^{\prime }\left(m,n\right)=\frac{\sum \sum I_1\left[p,q\right]\times I_2\left[p,q\right]}{\sqrt{\sum \sum I_1^2\left[p,q\right]}\sqrt{\sum \sum I_2^2\left[p,q\right]}}& \left(80\right)\end{array}$
where c′(m,n) is the normalized cross-correlation coefficient for the validation cell (m,n), m is an integer 1 to 4 corresponding to the column of the validation cell whose correlation coefficient is being calculated, n is an integer 1 to 4 corresponding to the row of the validation cell whose correlation coefficient is being calculated, p and q are matrix element markers, I₁[p,q] are elements of the cell in column m and row n of the 128×128 portion of the realigned image shown in block 1181 of FIG. 47, and I₂[p,q] are elements of the cell in column m and row n of the 128×128 portion of G_i(x,y) shown in block 1179 of FIG. 47. In that figure, p=1 to 32 and q=1 to 32, and the sums shown in Equation 80 are performed over p and q. The cross-correlation coefficient of Equation 80 is similar to an auto-correlation in the sense that a subsequent image is realigned with a prior image based on the determined misalignment correction so that, ideally, the aligned images appear to be identical. A low value of c′(m,n) indicates a mismatching between two corresponding cells. The misalignment correction determination is then either validated or rejected based on the values of the 16 correlation coefficients computed in step 1194 of FIG. 47. For example, each correlation coefficient may be compared against a threshold maximum value. This corresponds to step 1122 of FIG. 45.

[0450] Step 1126 of FIG. 45 represents comparing corresponding validation cells from G_j(x,y) and G_o(x,y) by computing correlation coefficients for each cell. This is represented schematically in FIG. 46 by blocks 1180, 1182, 1184, 1186, and 1188 for the case where j=i+1. First, a 128×128 pixel central portion of the realigned G_i+1(x,y) is selected, and the corresponding 128×128 pixel central portion of G_o(x,y) is selected, as shown in blocks 1182 and 1180 of FIG. 46. An exemplary 128×128 pixel validation region 1090 is shown in FIG. 44B. Then, one embodiment comprises computing a correlation coefficient for each of the 16 validation cells. An exemplary validation cell from each of the realigned G_i+1(x,y) matrix 1182 and Go(x,y) matrix 1180 is shown in blocks 1186 and 1184 of FIG. 46. The validation cells are as depicted in the 32×32 pixel divisions 1092 of the 128×128 pixel validation region 1090 of FIG. 44B. Other embodiments use different numbers of and/or different sizes of validation cells. Correlation coefficients are computed for each of the 16 cells, as shown in block 1188 of FIG. 46. Each correlation coefficient is a normalized “auto”-correlation coefficient as shown in Equation 80 above, where I_i[p,q] are elements of the cell in column m and row n of the 128×128 portion of the realigned subsequent image shown in block 1182 of FIG. 46, and I₂[p,q] are elements of the cell in column m and row n of the 128×128 portion of G_o(x,y) shown in block 1180 of FIG. 46. A low value of c′(m,n) indicates a mismatching between two corresponding cells. The misalignment determination is then either validated or rejected based on the values of the 16 correlation coefficients computed in step 1188 of FIG. 46. This corresponds to step 1128 of FIG. 45.

[0451] In one embodiment, determinations of misalignment correction and validation of these determinations as shown in each of FIG. 45, FIG. 46, and FIG. 47 are performed using a plurality of the images in sequence. In one embodiment, determinations of misalignment correction and validations thereof are performed while images are being obtained, so that an examination in which a given sequence of images is obtained may be aborted before all the images are obtained. In some embodiments, a misalignment correction is determined, validated, and compensated for by adjusting the optical signal detection device obtaining the images. In certain embodiments, an adjustment of the optical signal detection device is made after each of a plurality of images are obtained. In certain embodiments, an adjustment, if required by the misalignment correction determination, is made after every image subsequent to the first image (except the last image), and prior to the next consecutive image. In one embodiment, a cervical tissue scan comprising a sequence of 13 images is performed using on-the-fly misalignment correction determination, validation, and camera adjustment, such that the scan is completed in about 12 seconds. Other embodiments comprise obtaining sequences of any number of images in more or less time than indicated here.

[0452] Each of steps 1122 and 1128 of the embodiment of FIG. 45 represents applying a validation algorithm to determine at least the following: (1) whether the misalignment correction can be made, for example, by adjusting the optical signal detection device, and (2) whether the misalignment correction determined is valid. In an exemplary embodiment, the validation algorithm determines that a misalignment correction cannot be executed during an acetowhitening exam conducted on cervical tissue in time to provide sufficiently aligned subsequent images, if either of conditions (a) or (b) is met, as follows: (a) d_i, the displacement between the current image “i” and the immediately preceding image “i-1” is greater than 0.55-mm or (b) Sum(d_i), the total displacement between the current image and the first image in the sequence, “o”, is greater than 2.5-mm. If either of these conditions is met, the spectral scan in progress is aborted, and another scan must be performed. If sufficient time remains within the optimal time window for obtaining spectral data, a fresh scan may begin immediately after a previous scan is aborted. Other embodiments may comprise the use of different validation rules. In one embodiment, if only condition (a) is met, the system retakes image “i” while continuing the spectral scan, and if condition (b) is met, the spectral scan is aborted and must be restarted if sufficient time remains within the optimal window.

[0453] In one embodiment, validation is performed for each determination of misalignment correction by counting how many of the correlation coefficients c′_r(m,n) shown in Equation 80 (corresponding to the 16 validation cells) is less than 0.5. If this number is greater than 1, the scan in progress is aborted. In one embodiment, if there are more than three correlation coefficients c′_r(m,n) less than 0.35, then the scan is aborted. Other embodiments comprise the use of different validation rules. Gradual changes in image features, such as acetowhitening of tissue or changes in glare, cause discrepancies which are reflected in the correlation coefficients of the validation cells, but which do not represent a spatial shift. Thus, in preferred embodiments, the validation is performed as shown in FIG. 47, where validation cells of consecutive images are used to calculate the correlation coefficients. In other embodiments, the validation is performed as shown in FIG. 46, where validation cells of a current image, “i”, and an initial image of the sequence, “o”, are used to calculate the correlation coefficients of Equation 80.

[0454] FIGS. 48A-F depict a subset of adjusted, filtered images 1200, 1204, 1208, 1212, 1216, and 1220 from a sequence of images of a tissue with an overlay of gridlines showing the validation cells used in validating the determinations of misalignment correction between the images, according to an illustrative embodiment of the invention. By performing validation according to FIG. 47, using consecutive images to calculate the correlation coefficients of Equation 80, the number of validation cells with correlation coefficient below 0.5 for the misalignment-corrected images of FIG. 48A-F is 0, 1, 0, 0, and 1 for images 1204, 1208, 1212, 1216, and 1220, respectively. Since none of the images have more than one coefficient below 0.5, this sequence is successful and is not aborted. There is only a gradually changing glare, seen to move within the validation region 1202, 1206, 1210, 1214, 1218, 1222 of each image. In an embodiment in which validation is performed as in FIG. 46, the number of validation cells with correlation coefficient below 0.5 for the misalignment-corrected images of FIG. 48A-F is 3, 4, 5, 5, and 6 for images 1204, 1208, 1212, 1216, and 1220, respectively. This is not a good result in this example, since the exam would be erroneously aborted, due only to gradual changes in glare or whitening of tissue, not uncompensated movement of the tissue sample.

[0455] Alternatively, validation cells that are featureless or have low signal-to-noise ratio are eliminated from consideration. Those cells can produce meaningless correlation coefficients. Featureless cells in a preferred embodiment are identified and eliminated from consideration by examining the deviation of the sum squared gradient of a given validation cell from the mean of the sum squared gradient of all cells as shown in Equation 83:
IF ssg₁(m,n)<Mean[ssg(m,n)]−STD[ssg(m,n)], THEN set c′₁(m,n)=1.0.  (83)
where c′₁(m,n) is the correlation of the given validation cell “1”, ssg₁(m,n)=ΣΣI₁²[p,q], m=1 to 4, n=1 to 4, I₁[p,q] is the matrix of values of the given validation cell “1”, p=1 to 32, q=1 to 32, the summations ΣΣ are performed over pixel markers p and q, Mean[ssg(m,n)] is the mean of the sum squared gradient of all 16 validation cells, and STD[ssg(m,n)] is the standard deviation of the sum squared gradient of the given validation cell “1” from the mean sum squared gradient. By setting c′₁(m,n)=1.0 for the given validation cell, the cell does not count against validation of the misalignment correction determination in the rubrics of either step 1122 or step 1128 of FIG. 45, since a correlation coefficient of 1.0 represents a perfect match.

[0456] If an image has large intensity differences between the upper and lower borders and/or the left and right borders of the image frame field, LoG filtering may result in “wraparound error.” A preferred embodiment employs an image blending technique such as “feathering” to smooth border discontinuities, while requiring only a minimal amount of additional processing time.

[0457] FIG. 49A depicts a sample image 1224 after application of a 9-pixel size [9×9] Laplacian of Gaussian filter (LoG 9 filter) on an exemplary image from a sequence of images of tissue, according to an illustrative embodiment of the invention. The filtered intensity values are erroneous at the top edge 1226, the bottom edge 1228, the right edge 1232, and the left edge 1230 of the image 1224. Since LoG frequency domain filtering corresponds to cyclic convolution in the space-time domain, intensity discontinuities between the top and bottom edges of an image and between the right and left edges of an image result in erroneous gradient approximations. These erroneous gradient approximations can be seen in the dark stripe on the right edge 1232 and bottom edge 1228 of the image 1224, as well as the light stripe on the top edge 1226 and the left edge 1230 of the image 1224. This often results in a misalignment correction determination that is too small, since changes between the images due to spatial shift are dwarfed by the edge effects. A preferred embodiment uses a “feathering” technique to smooth border discontinuities and reduce “wraparound error.”

[0458] Feathering comprises removal of border discontinuities prior to application of a filter. In preferred embodiments, feathering is performed on an image before LoG filtering, for example, between steps 1100 and 1102 in FIG. 45. In embodiments where LoG filtering is performed in the frequency domain (subsequent to Fourier transformation), feathering is preferably performed prior to both Fourier transformation and LoG filtering. For two-dimensional image intensity (luminance) functions I₁(x,y) and I₂(x,y) that are discontinuous at x=x₀, an illustrative feathering algorithm is as follows:

[0459] $\begin{array}{cc}\begin{array}{c}{I}_1^{\prime }\left(x,y\right)=I_1\left(x,y\right)·f\left(\frac{x-x_0}{d}+0.5\right)\mathrm{and}\\ I_2^{\prime }\left(x,y\right)=I_2\left(x,y\right)·\left(1-f\left(\frac{x-x_0}{d}+0.5\right)\right),\\ f\left(x\right)=\{\begin{array}{cc}0& x<0\\ 3x^2-2x^3& 0\le x\le 1\\ 0& x>1,\end{array}\end{array}& \left(82\right)\end{array}$
where I₁′(x,y) and I₂′(x,y) are the intensity (luminance) functions I₁(x,y) and I₂(x,y) after applying the feathering algorithm of Equation 82, and d is the feathering distance chosen. The feathering distance, d, adjusts the tradeoff between removing wraparound error and suppressing image content.

[0460] FIG. 49B depicts the application of both a feathering technique and a LoG filter on the same unfiltered image used in FIG. 49A. The feathering is performed to account for border processing effects, according to an illustrative embodiment of the invention. Here, a feathering distance, d, of 20 pixels was used. Other embodiments use other values of d. The filtered image 1234 of FIG. 49B does not display uncharacteristically large or small gradient intensity values at the top edge 1236, bottom edge 1238, right edge 1242, or left edge 1240, since discontinuities are smoothed prior to LoG filtering. Also, there is minimal contrast suppression of image detail at the borders. Pixels outside the feathering distance, d, are not affected. The use of feathering here results in more accurate determinations of misalignment correction between two images in a sequence of images.

[0461] Another method of border smoothing is multiplication of unfiltered image data by a Hamming window. In some embodiments, a Hamming window function is multiplied to image data before Fourier transformation so that the border pixels are gradually modified to remove discontinuities. However, application of the Hamming window suppresses image intensity as well as gradient information near the border of an image.

[0462] FIG. 50A is identical to FIG. 49A and depicts the application of a LoG 9 filter on an exemplary image from a sequence of images of tissue according to an illustrative embodiment of the invention. The filtered intensity values are erroneous at the top edge 1226, the bottom edge 1228, the right edge 1232, and the left edge 1230 of the image 1224.

[0463] FIG. 50B depicts the application of both a Hamming window and a LoG 9 filter on the same unfiltered image used in FIG. 50A. Hamming windowing is performed to account for border processing effects, according to an illustrative embodiment of the invention. Each of the edges 1246, 1248, 1250, 1252 of the image 1244 of FIG. 50B no longer show the extreme filtered intensity values seen at the edges 1226, 1228, 1230, 1232 of the image 1224 of FIG. 50A. However, there is a greater suppression of image detail in FIG. 50B than in FIG. 49B. Thus, for this particular embodiment, application of the feathering technique is preferred over application of Hamming windowing.

[0464] One embodiment includes removing cyclic convolution artifacts by zero padding the image prior to frequency domain filtering to assure image data at an edge would not affect filtering output at the opposite edge. This technique adds computational complexity and may increase processing time.

[0465] FIGS. 51A-F depict the determination of a misalignment correction between two images using methods including the application of LoG filters of various sizes, as well as the application of a Hamming window technique and a feathering technique, according to illustrative embodiments of the invention. Image 1254 and image 1256 of FIGS. 51A-B are consecutive images from a sequence of images of cervix tissue obtained during a diagnostic exam, each with a pixel resolution of about 0.054-mm. FIGS. 51 C-F depict the application of four different image filtering algorithms: (1) Hamming window with LoG 9 filtering, (2) feathering with LoG 9 filtering, (3) feathering with LoG 21 filtering, and (4) feathering with LoG 31 filtering. Each of these algorithms are implemented as part of a misalignment correction determination and validation technique as illustrated in FIG. 45 and FIG. 47, and values of d, and dy between images 1254 and 1256 of FIGS. 51A-B are determined using each of the four filtering algorithms. For image 1254, each of the four different image filtering algorithms (1)-(4) listed above are applied, resulting in images 1258, 1262, 1266, and 1270, respectively, each having 256×256 pixels. The four different image filtering algorithms are also applied for image 1256, resulting in images 1260, 1264, 1268, and 1272, respectively, each having 256×256 pixels. Values of (d_x, d_y) determined using Hamming+LoG 9 filtering are (−7, 0), expressed in pixels. Values of (d_x, d_y) determined using feathering +LoG 9 filtering are (−2, −10). Values of (d_x, d_y) determined using feathering +LoG 21 filtering are (−1, −9). Values of (d_x, d_y) determined using feathering +LoG 31 filtering are (0, −8). All of the displacement values determined using feathering are close in this embodiment, and agree well with visually-verified displacement. However, in this example, the displacement values determined using Hamming windowing are different from those obtained using the other three filtering methods, and result in a misalignment correction that does not agree well with visually-verified displacement. Thus, for this example, feathering works best since it does not suppress as much useful image data.

[0466] The effect of the filtering algorithm employed, as well as the choice of validation rules are examined by applying combinations of the various filtering algorithms and validation rules to pairs of sequential images of tissue and determining the number of “true positives” and “false positives” identified. A true positive occurs when a bad misalignment correction determination is properly rejected by a given validation rule. A false positive occurs when a good misalignment correction determination is improperly rejected as a failure by a given validation rule. The classification of a validation result as a “true positive” or a “false positive” is made by visual inspection of the pair of sequential images. In preferred embodiments, whenever true failures occur, the scan should be aborted. Some examples of situations where true failures occur in certain embodiments include image pairs between which there is one or more of the following: a large non-translational deformation such as warping or tilting; a large jump for which motion tracking cannot compute a correct translational displacement; rotation greater than about 3 degrees; situations in which a target laser is left on; video system failure such as blur, dark scan lines, or frame shifting; cases where the image is too dark and noisy, in shadow; cases where a vaginal speculum (or other obstruction) blocks about half the image; other obstructions such as sudden bleeding.

[0467] In one embodiment, a set of validation rules is chosen such that true positives are maximized and false positives are minimized. Sensitivity and specificity can be adjusted by adjusting choice of filtering algorithms and/or choice of validation rules. Table 4 shows the number of true positives (true failures) and false positives (false failures) determined by a validation rule as depicted in FIG. 45 and FIG. 47 where validation is determined using consecutive images. Table 4 shows various combinations of filtering algorithms and validation rules. The four filtering algorithms used are (1) Hamming windowing with LoG 9 filtering, (2) feathering with LoG 9 filtering, (3) feathering with LoG 21 filtering, and (4) feathering with LoG 31 filtering. The values, c′(m,n), correspond to the normalized “auto”-correlation coefficient of Equation 80 whose value must be met or exceeded in order for a validation cell to “pass” in an embodiment. The “Number Threshold” column indicates the maximum number of “failed” validation cells, out of the 16 total cells, that are allowed for a misalignment correction determination to be accepted in an embodiment. If more than this number of validation cells fail, then the misalignment correction determination is rejected.

[0468]

TABLE 4
True positives and false positives of validation determinations
for embodiments using various combinations of filtering
algorithms and validation rules.
			Number
		c′(m, n)	Threshold	TP	FP
	Hamming LoG 9	−0.1	1	34	28
	Feathering LoG 9	−0.1	3	19	17
	Feathering LoG 21	0.3	2	46	10
		0.35	3	52	4
	Feathering LoG 31	0.5	3	48	3

[0469] For the given set of cervical image pairs on which the methods shown in Table 4 were applied, feathering performs better than Hamming windowing, since there are more true positives and fewer false positives. Among different LoG filter sizes, LoG 21 and LoG 31 performs better than LoG 9 for both tracking and validation here. The LoG 21 filter is more sensitive to rotation and deformation than the LoG 31 filter for these examples. One embodiment of the determination and validation of misalignment corrections between 256×256 pixel portions of images of cervical tissue with pixel resolution of about 0.054-mm employs one or more of the following: (1) use of feathering for image border processing, (2) application of LoG 21 filter, (3) elimination of validation cells with low signal-to-noise ratio, and (4) use of consecutive images for validation.

[0470] A tissue characterization system as shown in FIG. 1 also may comprise arbitrating between two or more redundant sets of spectral data as depicted in step 128 of FIG. 1. In one embodiment shown in FIG. 1, step 128 includes arbitrating between two sets of broadband reflectance data obtained in step 104 during a spectral scan for each interrogation point of a tissue sample. Data are obtained at each interrogation point using light incident to the interrogation point at two different angles, as depicted in FIG. 8. In this way, if only one set of reflectance data is affected by an artifact such as glare or shadow, the other set can be used in tissue classification, for example, in step 132 of FIG. 1. The arbitration step 128 in FIG. 1 determines whether either of the two sets of reflectance spectral data at each point is affected by an artifact. Step 128 also determines a single set of reflectance data from each interrogation point to be used in tissue classification if at least one of the two sets is acceptably unaffected by an artifact. As used here, artifacts identified in the arbitration step 128 of FIG. 1 include, for example, both lighting artifacts and obstruction artifacts—such as glare, shadow, blood, mucus, a speculum, smoke tube tissue, and/or os tissue.

[0471] In the embodiment shown in FIG. 1, step 128 additionally includes a first-level “hard masking” of certain interrogation points. For example, interrogation points are considered “indeterminate” where values of both sets of reflectance spectral data and/or values of the set of fluorescence data are low due to shadow or an obstruction. Additional spectral masks, both hard masks and soft masks, are determined in one embodiment in step 130 of FIG. 1. As discussed herein, hard-masking of data includes eliminating identified, potentially non-representative data from further consideration and identifying the corresponding tissue region as “indeterminate”, while soft-masking includes applying a weighting function or weighting factor to identified, potentially non-representative data so that the importance of the data as a diagnostic indicator of a tissue region in a tissue classification algorithm is thereby reduced. A point that is soft-masked is not necessarily identified as “indeterminate”.

[0472] The diagram 284 of FIG. 8 shows that a misalignment of the probe 142 may create conditions where either or both of the top and bottom speculum blades 286 block part or all of the illumination path from either or both of the intersecting upper and lower cones of illuminating light 196,198, thereby affecting the spectral data obtained for the region 250 of the tissue sample 194. The speculum blades, or other obstructions present during a spectral scan, may physically obstruct the region 250 being analyzed, or may partially obstruct the light illuminating the region 250 causing a shadow. In either case, the spectral data obtained may be adversely affected and rendered unusable for characterizing the region of the tissue sample. Obtaining multiple sets of spectral data using illumination from sources at various positions and angles improves the chances of obtaining at least one set of spectral data that is not affected by glare, shadow, and/or obstructions.

[0473] FIG. 52 shows a graph 1276 depicting exemplary mean values of reflectance spectral data 1278 as a function of wavelength 1280 for tissue regions affected by glare 1282, tissue regions affected by shadow 1284, and tissue regions affected by neither glare nor shadow 1286 according to an illustrative embodiment of the invention. The reflectance spectral data 1278 represent the fraction of incident light that is reflected from the sample. The graph 1276 shows that the reflectance values of a region of tissue affected by glare 1282 are higher at all measured wavelengths than the reflectance of a region of tissue not affected by glare 1286. The graph 1276 also shows that the reflectance values of a region of tissue with illumination partially blocked by a speculum blade such that the region is in shadow 1284, are lower at all measured wavelengths than the reflectance of a region of tissue not affected by shadow 1286. The shapes of all three curves 1282, 1284, 1286 are different. In this example, the data affected by glare or shadow may not be usable to determine a condition or characteristic of the region of the sample, if the data are not representative of the region of the tissue sample. Hence, glare and shadow may adversely affect spectral data obtained for a region of a tissue sample.

[0474] In one embodiment, step 104 of FIG. 1 comprises obtaining one fluorescence spectrum and two broadband reflectance spectra at each of a plurality of scan locations of the sample tissue (interrogation points). Here, a spectrum refers to a collection of spectral data over a range of wavelengths. In one embodiment method, spectral data are collected over a range of wavelengths between 360 and 720 nm in 1 nm increments. In other embodiments, the range of wavelengths lies anywhere between about 190 nm and 1100 nm. Here, the two reflectance spectra are referred to as the BB1 (broadband one) and BB2 (broadband two) spectra. BB1 and BB2 differ in the way that the tissue is illuminated at the time the spectral data are obtained as described below. In the embodiment shown in FIG. 6, the probe head 192 has 4 illumination sources 222, 224, 226, 228 located circumferentially about the collection optics 200. Two sources are above 222, 224 and two are below the horizontal plane 226, 228, as illustrated in the second arrangement 212 of FIG. 6. The two upper sources are used to obtain BB1 spectra and the two lower sources are used to obtain BB2 spectra. Since the upper and lower sources illuminate a region of the tissue sample using light incident to the region at different angles, an artifact—for example, or shadow—may affect one of the two reflectance spectra obtained for the region, while the other reflectance spectrum is unaffected. For example, during acquisition of spectral data, the BB1 spectrum may be unaffected by an artifact even if the BB2 spectrum is adversely affected by the artifact. In such a case, BB1 spectral data may be used to characterize the condition of the region of tissue, for example, in step 132 of FIG. 1, even though the BB2 data is not representative of the region. In other embodiments, the BB1 and BB2 spectra comprise one or more other types of spectral data, such as absorbance spectra, adsorption spectra, transmission spectra, fluorescence spectra, and/or other types of optical and atomic emission spectra.

[0475] FIG. 53 shows a graph 1287 depicting mean values and standard deviations of broadband reflectance spectral data using the BB1 channel light source for regions confirmed as being obscured by blood, obscured by mucus, obscured by glare from the BB1 source, obscured by glare from the BB2 source, or unobscured, according to an illustrative embodiment of the invention. Various sample test points corresponding to regions of tissue from patient scans were visually identified as having blood, mucus, or glare present. A sample point was identified as having blood present if it was completely covered by blood and if there was no glare. A sample point was identified as having mucus present if it was completely covered by mucus and if there was no glare. A sample point was identified as having glare based on visual evidence of glare and large reflectance values in at least one of the two sets of reflectance spectral data (the BB1 spectrum or the BB2 spectrum). FIG. 53 shows the range of BB1 reflectance values 1288 for a given category of the sample test points which lie within one standard deviation of the mean for the category, plotted as a function of wavelength 1290. FIG. 53 shows ranges of BB1 reflectance values 1288 for each of the following categories of sample test points: those identified as having blood present 1292, those identified as having mucus present 1294, those identified as having glare from the BB1 illumination source 1296, those identified as having glare from the BB2 illumination source 1298, and those identified as unobstructed tissue 1300.

[0476] Similarly, FIG. 54 shows a graph 1301 depicting mean values and standard deviations of broadband reflectance spectral data using the BB2 channel light source for regions confirmed as being obscured by blood 1304, obscured by mucus 1306, obscured by glare from the BB1 source 1308, obscured by glare from the BB2 source 1310, or unobscured 1312, according to an illustrative embodiment of the invention. FIG. 54 shows the range of BB2 reflectance values 1302 for a given category of the sample test points which lie within one standard deviation of the mean for the category, plotted as a function of wavelength 1290. FIG. 54 shows ranges of BB2 reflectance values 1302 for each of the following categories of sample test points: those identified as having blood present 1304, those identified as having mucus present 1306, those identified as having glare from the BB1 illumination source 1308, those identified as having glare from the BB2 illumination source 1310, and those identified as unobstructed tissue 1312.

[0477] FIGS. 53 and 54 show that a region with glare from one illumination source does not necessarily have high reflectance values corresponding to data obtained using the other illumination source. For example, in FIG. 53, the range of BB1 reflectance values 1288 of points with visual evidence of glare from the BB2 source 1298 is similar to the range of BB1 reflectance values 1288 of unobstructed tissue 1300. Similarly, in FIG. 54, the range of BB2 reflectance values 1302 of points demonstrating glare from the BB1 source 1308 is similar to the range of BB2 reflectance values 1302 of unobstructed tissue 1312. Therefore, one of the two sets of reflectance spectral data may be useful in characterizing the tissue even if the other of the two sets is corrupted by an artifact, such as glare.

[0478] It may also be desirable to determine spectral characteristics caused by various artifacts so that data corresponding to a region affected by a given artifact may be identified or to determine a spectral characteristic of an- artifact based on the spectral data itself, without having to rely on other visual evidence of a given artifact. In order to determine these spectral characteristics, an embodiment of the invention comprises using spectral data known to be affected by a given artifact based on visual evidence, as well as spectral data known not to be affected by an artifact. Techniques that may be used to identify spectral characteristics and/or to develop classification rules determining whether given data are affected by an artifact include, for example, discriminant analysis (linear, nonlinear, multivariate), neural networks, principal component analysis, and decision tree analysis. One embodiment comprises determining a particular wavelength that gives the greatest difference between the artifact-affected spectral data (the outlier) and spectral data from corresponding nearby tissue that is known to be unaffected by the artifact (the tissue). Alternatively, the embodiment comprises determining a wavelength that gives the largest difference between the outlier and the tissue, as weighted by a measure of variability of the data. In one embodiment, this method locates where the difference between the mean reflectance for the outlier and the tissue is at a maximum relative to the difference between the standard deviations for the outlier data and the tissue data. In one embodiment, the method determines a maximum value of D as a function of wavelength, where D is the difference given in Equation 83 below:

[0479] $\begin{array}{cc}D\left(\lambda \right)=\frac{{\mu \left(\mathrm{BB}\left(\lambda \right)\right)}_{\mathrm{Outlier}}-{\mu \left(\mathrm{BB}\left(\lambda \right)\right)}_{\mathrm{Tissue}}}{\sqrt{{{\sigma }^2\left(\mathrm{BB}\left(\lambda \right)\right)}_{\mathrm{Outlier}}+{{\sigma }^2\left(\mathrm{BB}\left(\lambda \right)\right)}_{\mathrm{Tissue}}}},& \left(83\right)\end{array}$
where μ(BB(λ))_Outlier is the mean of a set of reflectance spectral data at wavelength λ known to be affected by a given artifact, μ(BB(λ))_Tissue is the mean of a set of reflectance spectral data at wavelength λ that is known not to be affected by the artifact, ((BB(λ))_Outlier is the standard deviation of the set of reflectance spectral data at wavelength λ known to be affected by the given artifact, and σ(BB(λ))_Tissue is the standard deviation of the set of reflectance spectral data at wavelength λ known not to be affected by the given artifact.

[0480] FIG. 55 shows a graph 1313 depicting the weighted difference 1314 between the mean reflectance values of glare-obscured regions and unobscured regions of tissue as a function of wavelength 1316, according to an illustrative embodiment of the invention. The weighted difference 1314 is as given in Equation 83. For the data sets used in FIG. 55, the wavelength providing the maximum value 1318 of D in Equation 83 is about 420 nm. Thus, exemplary spectral characteristics identifiable with this set of glare-obscured “outlier” data include the reflectance spectral data at around 420 nm, and any deviation of this data from reflectance spectral “tissue” data for unobscured regions of correspondingly similar tissue at around 420 nm. This embodiment uses reflectance spectral data. Other embodiments may use other types of spectral data, including fluorescence data.

[0481] FIG. 56 shows a graph 1319 depicting the weighted difference 1314 between the mean reflectance values of blood-obscured regions and unobscured regions of tissue as a function of wavelength 1316, according to an illustrative embodiment of the invention. The weighted difference is as given in Equation 83. For the data sets used in FIG. 56, the wavelength providing the maximum value 1320 of D in Equation 83 is about 585 nm.

[0482] Thus, exemplary spectral characteristics identifiable with this set of blood-obscured “outlier” data include the reflectance spectral data at about 585 nm, and any deviation of this data from reflectance spectral “tissue” data for unobscured regions of correspondingly similar tissue at about 585 nm. This embodiment uses reflectance spectral data. Other embodiments may use other types of spectral data, including fluorescence spectral data.

[0483] FIG. 57 shows a graph 1321 depicting the weighted difference 1314 between the mean reflectance values of mucus-obscured regions and unobscured regions of tissue as a function of wavelength 1316, according to an illustrative embodiment of the invention. The weighted difference is as given in Equation 83. For the data sets used in FIG. 57, the wavelength providing the maximum value 1322 of D in Equation 83 is about 577 nm. Thus, exemplary spectral characteristics identifiable with this set of mucus-obscured “outlier” data include the reflectance spectral data at about 577 nm, and any deviation of this data from reflectance spectral “tissue” data for unobscured regions of correspondingly similar tissue at about 577 nm. This embodiment uses reflectance spectral data. Other embodiments may use other types of spectral data, including fluorescence spectral data.

[0484] One illustrative embodiment comprises determining two wavelengths where the ratio of spectral data at the two wavelengths is most different for the artifact-affected spectral data (the “outlier”) and spectral data from corresponding nearby tissue that is known to be unaffected by the artifact (the “tissue”). Alternatively, the method comprises determining two wavelengths where the ratio of spectral data at the two wavelengths weighted by a measure of variability is most different for the outlier data and the tissue data. In one embodiment, the method comprises determining a maximum value of D as a function of wavelength, where D is the difference given in Equation 84 below:

[0485] $\begin{array}{cc}D=\frac{{\mu \left(\mathrm{BB}\left(\lambda \right)/\mathrm{BB}\left({\lambda }^{\prime }\right)\right)}_{\mathrm{Outlier}}-{\mu \left(\mathrm{BB}\left(\lambda \right)/\mathrm{BB}\left({\lambda }^{\prime }\right)\right)}_{\mathrm{Tissue}}}{\sqrt{{{\sigma }^2\left(\mathrm{BB}\left(\lambda \right)/\mathrm{BB}\left({\lambda }^{\prime }\right)\right)}_{\mathrm{Outlier}}+{{\sigma }^2\left(\mathrm{BB}\left(\lambda \right)/\mathrm{BB}\left({\lambda }^{\prime }\right)\right)}_{\mathrm{Tissue}}}},& \left(84\right)\end{array}$
where μ(BB(λ)/BB(λ))_Outlier is the mean of the ratios of reflectance at wavelength λ and reflectance at wavelength λ′ for a set of reflectance spectral data known to be affected by a given artifact, μ(BB(λ)/BB(λ))_Tissue is the mean of the ratios of reflectance at wavelength λ and reflectance at wavelength λ′ for a set of reflectance spectral data that is known not to be affected by the given artifact, σ(BB(λ)/BB(λ′))_Outlier is the standard deviation of the ratios of reflectance at wavelength λ and reflectance at wavelength λ′ for a set of reflectance spectral data known to be affected by the given artifact, and μ(BB(λ)/BB(λ′))_Tissue is the standard deviation of the ratios of reflectance at wavelength λ and reflectance at wavelength λ′ for a set of reflectance spectral data known not to be affected by the given artifact.

[0486] FIG. 58 shows a graph 1323 depicting a ratio of the weighted differences 1324 between the mean reflectance values of glare-obscured regions and unobscured regions of tissue at two wavelengths, a numerator wavelength 1326 and a denominator wavelength 1328, according to an illustrative embodiment of the invention. The weighted difference 1324 is as given in Equation 84. For the data sets used in FIG. 58, the two wavelengths providing the maximum value of D in Equation 84 are about 401 nm (numerator) and about 404 nm (denominator). Thus, exemplary spectral characteristics identifiable with s this set of glare-obscured “outlier” data include the ratio of reflectance spectral data at about 401 nm and the reflectance spectral data at about 404 nm, as well as any deviation of this ratio from those of corresponding regions of similar but unobscured tissue. This embodiment uses reflectance spectral data. Other embodiments may use other types of spectral data, including fluorescence data.

[0487] FIG. 59 shows a graph 1325 depicting a ratio of the weighted differences 1324 between the mean reflectance values of blood-obscured regions and unobscured regions of tissue at two wavelengths, a numerator wavelength 1326 and a denominator wavelength 1328, according to an illustrative embodiment of the invention. The weighted difference is as given in Equation 84. For the data sets used in FIG. 59, the two wavelengths providing the maximum value of D in Equation 84 are about 595 nm (numerator) and about 718 nm (denominator). Thus, an exemplary spectral characteristic identifiable with this set of blood-obscured “outlier” data includes the ratio of the reflectance spectral data at about 595 nm and the reflectance spectral data about 7188 nm. This embodiment uses reflectance spectral data. Other embodiments may use other types of spectral data, including fluorescence data.

[0488] FIG. 60 shows a graph 1327 depicting a ratio of the weighted differences 1324 between the mean reflectance values of mucus-obscured regions and unobscured regions of tissue at two wavelengths, a numerator wavelength 1326 and a denominator wavelength 1328, according to an illustrative embodiment of the invention. The weighted difference is as given in Equation 84. For the data sets used in FIG. 60, the two wavelengths providing the maximum value of D in Equation 84 are about 545 nm (numerator) and about 533 nm (deno minator). Thus, an exemplary spectral characteristic identifiable with this set of mucus-obscured “outlier” data includes the ratio of the reflectance spectral data at about 545 nm and the reflectance spectral data at about 533 nm. This embodiment uses reflectance spectral data. Other embodiments may use other types of spectral data, including fluorescence data.

[0489] Another type of lighting artifact which may obscure spectral data is shadow, which may be caused, for example, by an obstruction blocking part of the light from an illumination source on the optical probe 142 of the embodiment apparatus. It may be important to differentiate between glare and shadow, so that spectral data representing unobstructed tissue can be properly identified. In an embodiment, broadband reflectance is expressed as the intensity of light diffusely reflected from a region of the tissue, I_t, over the intensity of incident light, I_o, at the region. When glare is measured in addition to light diffusely reflected from the tissue, a percentage of the original intensity of incident light is included in the tissue reflectance measurement, so that the “reflectance” reading of a region of a sample experiencing glare, R_g(λ), may be expressed as in Equation 85:
R_g(λ)−(I_t(λ)+αI_o(λ))/I_o(λ),  (85)
where α is a real number between 0.0 and 1.0; I_t(λ) is the intensity of light diffusely reflected from the region of tissue at wavelength λ, and I_o(λ) is the intensity of light incident on the region of the sample at wavelength λ. The intensity of the specularly-reflected light is αI_o(λ). When the region of the sample is shadowed, only a portion of the incident intensity reaches the region. Thus, the “reflectance” reading of a region of a sample experiencing shadow, R_s(λ), may be expressed as in Equation 86:
R_s(λ)=βI_t(λ)/I_o(λ).  (86)
where β is a real number between 0.0 and 1.0; I_t(λ) is the intensity of light at wavelength λ diffusely reflected from the region of tissue with an incident light intensity of I_o(λ), and I_o(λ) is the intensity of light at wavelength λ that would be incident on the region of the sample if unshadowed.

[0490] In one embodiment, the arbitration in step 128 of FIG. 1 comprises determining if only one set of a pair of sets of spectral data is affected by a lighting artifact, such as glare or shadow, each set having been obtained using light incident on the sample at a unique angle. If it is determined that only one set of a pair of sets of spectral data is affected by the artifact, then the other set of spectral data may be used in the determination of a characteristic of the region of the sample, for example. In one embodiment, it is determined that there is evidence of a lighting artifact in the spectral data. Such evidence may be a large difference between the reflectance measurements of the two sets of spectral data. If such evidence exists, then one of the reflectance measurements will either be R_g or R_s, as given by Equation 85 and Equation 86. In cases where members of only one set are affected by a lighting artifact, the remaining set of reflectance measurements may be expressed as R, the intensity of light diffusely reflected from the region of the tissue, I_t, divided by the intensity of light incident on the region of the tissue, I_o. In an embodiment method, the larger of the two reflectance measurements corresponding to a given wavelength is divided by the smaller. In cases where only one of the sets is affected by a lighting artifact, the resulting quotient will be either R_g/R, which is equal to 1+αI_o(λ)/I_t(λ), or R/R_s, which is equal to the constant, 1/β. If glare is present, the value of the quotient will depend on wavelength and the plot of the quotient as a function of wavelength should look like an inverted unobstructed tissue broadband signal because of the αI_o(λ)/I_t(λ) term. If shadow is present, the plot of the quotient should be constant across the spectrum.

[0491] FIG. 61 shows a graph 1332 depicting as a function of wavelength 1336 mean values and confidence intervals of a ratio 1334 of BB1 and BB2 broadband reflectance spectral values (larger value divided by smaller value) for regions confirmed as being either glare-obscured or shadow-obscured tissue, according to an illustrative embodiment to of the invention. The shadow points 1338 yield a nearly constant value, while the glare points 1340 vary over the range of wavelength 1336 in a manner that resembles the inverse of unobstructed tissue reflectance. Thus, FIG. 61 illustrates an embodiment in which it is determined whether only one set of a pair of sets of spectral data is affected by either glare or shadow, such that the other set is unaffected by glare or shadow and may be used to determine a characteristic of the tissue, for example. In an embodiment, the method comprises differentiating between glare and shadow by observing the steep slope of glare-affected reflectance spectral measurements between about 577 nm and 599 nm, for example, compared to the nearly flat slope of shadow-affected reflectance spectral measurements at those wavelengths, as seen in FIG. 61.

[0492] In one embodiment, the arbitration in step 128 of FIG. 1 includes applying and/or developing spectral artifact classification rules (metrics) using spectral data, including one or more sets of fluorescence and broadband reflectance data obtained using light at one or more angles. In one embodiment, one set of fluorescence data and two sets of reflectance data are obtained from a given region of a tissue sample (interrogation point), where each of the two sets of reflectance data are obtained using light incident on the region at a different angle. These metrics determine what data is representative of a given region of tissue. By varying the metrics, desired levels of sensitivity and selectivity of a resulting tissue characterization using tissue-representative data may be achieved.

[0493] The following metrics are applied in one embodiment of the arbitration in step 128 of FIG. 1 and were determined using the embodiments discussed above. These metrics were developed using one set of fluorescence data and two sets of reflectance data, BB1 and BB2, for samples of cervical tissue. Other embodiments use other combinations of spectral data sets. Each of the two sets of reflectance data used in the following metrics were obtained using light incident to a region of a sample at different angles. An embodiment of the invention uses any or all of the metrics listed below to determine if any set of data should be eliminated from use in determining a characteristic of a region of tissue, due to the presence of a spectral artifact. In an embodiment of the invention, wavelengths within a range of the wavelengths shown below are used. In one embodiment, this range about the wavelengths is about ±10 nm. In an embodiment of the invention, only certain parts of the metrics shown below are used. In one embodiment, only a portion of a given set of spectral data are eliminated, not the entire set. In one embodiment, BB1 and BB2 reflectance data are obtained, but fluorescence data is not. Here, “eliminate data” means to eliminate data from consideration in an analysis, for example, an analysis to determine a condition of a region. It is possible to change sensitivity and selectivity of a tissue diagnostic algorithm by varying the metrics below, for instance by changing one or more of the threshold constants. Such variations are within an embodiment of this invention. The metrics for one exemplary embodiment are as follows:

[0494] Glare Metric #1: Eliminate BB1 Data IF

• • [0495] I. {BB1(419)>0.25 AND BB1(699)>0.51}OR BB1(529)/BB1(543)<1.0;
  • [0496] OR II. Max{|ΔBB|/avgBB}(370-710)>0.25 AND BB1(419)>0.18 AND BB1(699)>0.51 AND {BB1(576)/BB2(576)}/{BB1(599)/BB2(599)}>1.1;
  • [0497] OR III. Max{|ΔBB|/avgBB}(370-710)>0.4 AND {BB1(576)/BB2(576)}/{BB1(599)/BB2(599)}>1.1 AND BB2(699)>0.3.

[0498] Glare Metric #2: Eliminate BB2 Data IF

• • [0499] I. {BB2(419)>0.25 AND BB2(699)>0.51}OR BB2(529)/BB2(543)<1.0;
  • [0500] OR II. Max{|ΔBB|/avgBB}(370-710)>0.25 AND BB2(419)>0.18 AND BB2(699)>0.51 AND {BB2(576)/BB1(576)}/{BB2(599)/BB1(599)}>1.1;
  • [0501] OR III. Max{|ΔBB|/avgBB}(370-710)>0.4 AND {BB2(576)/BB1 (576)}1{BB2(599)/BB1(599)}>1.1 AND BB1(699)>0.3.

[0502] Shadow Metric #1: Eliminate BB1 Data IF

• • [0503] I. BB2(499)>BB1(499) AND Max{|ΔBB|/avgBB}(370-710)>0.25 AND BB1(499)<0.05;
  • [0504] OR II. Max{|ΔBB|/avgBB}(370-710)>0.5 AND {BB1(576)/BB2(576)}/{BB1(599)/BB2(599)}<1.1 AND BB2(576)>BB1(576) AND BB1(419)<0.2.

[0505] Shadow Metric #2: Eliminate BB2 Data IF

• • [0506] I. BB1(499)>BB2(499) AND Max{|ΔBB|/avgBB}(370-710)>0.25 AND BB2(499) <0.05;
  • [0507] OR II. Max{|ΔBB|/avgBB}(370-710)>0.5 AND {BB2(576)/BB1 (576)}/{BB2(599)/BB1 (599)}<1.1 AND BB1(576)>BB2(576) AND BB2(419)<0.2.

[0508] Low Signal: Eliminate BB1, BB2, and Fl Data IF

• • [0509] I. Fl(479)<3.5 counts/μJ (where mean fluorescent intensity of normal squamous tissue is about 70 counts/μJ at about 450 nm);
  • [0510] OR II. BB1(499)<0.035 & BB2(499)<0.035.
    where BB1 (X) is the BB1 reflectance spectrum measurement at wavelength X, BB2(X) is the BB2 reflectance spectrum measurement at wavelength X, Max{|ΔBB|/avgBB}(370-710) indicates the maximum of {the absolute value of the difference between the BB1 and BB2 reflectance spectrum measurements divided by the average of the BB1 and BB2 measurements at a given wavelength} over the range of about 370 to 710 nm, and F1(X) is the fluorescence spectrum measurement at wavelength X. The following are notes regarding the Metrics listed above and apply to a preferred embodiment, subject to the variations described above:

[0511] Glare Metric #1 and Glare Metric #2

• • [0512] Level I: Broadband measurements are generally greater than about 0.25 at about 419 nm and greater than about 0.51 at about 699 nm only when there is glare in the channel (i.e. BB1 or BB2). The lack of a downward slope between about 499 and about 543 nm is also a strong indication that the broadband measurements are affected by glare.
  • [0513] Level II: Large percentage differences in the broadband measurements combined with higher than average reflectance at about 419 nm and about 699 nm also indicates the presence of glare. The presence of a slope when the broadband measurements at about 576 nm and about 599 mn are divided is further confirmation that glare is present.
  • [0514] Level III: A maximum broadband percent difference that is larger than about 0.4 indicates that there is a lighting artifact present. The presence of a slope when the broadband measurements at about 576 and about 599 nm are divided and an off-channel broadband greater than about 0.3 at about 699 nm reveals that the lighting artifact is due to glare instead of shadow.
  • [0515] If a point is identified as glare in one channel, then subsequently identified as glare in both channels, both broadband measurements should be eliminated.

[0516] Shadow Metric #1 and Shadow Metric #2

• • [0517] Level I: Broadband measurements that are shadowed generally will have a large percent difference between BB1 and BB2 and a low reflectance at about 499 nm.
  • [0518] Level II: A maximum broadband percent difference that is larger than about 0.5 indicates that there is a lighting artifact present. Lacking a large slope when the broadband measurements at about 576 and about 599 nm are divided and an off-channel broadband less than about 0.2 at about 419 nm reveals that the point is shadow instead of glare.
  • [0519] Cases where both BB and Fl measurements should be eliminated:

[0520] Low Signal

• • [0521] Broadband measurements lower than about 0.035 at about 449 nm or fluorescence measurements lower than about 3.5 at about 479 nm indicate that the measurements are not coming from tissue, but rather from blood, the os, smoke tube, speculum, or another obstruction. Sites with significant shadowing in both broadband charmels are also identified with this metric. Because of the uncertainty of the tissue being measured, the reflectance and fluorescence data from that point are assumed invalid, regardless of whether it was identified by fluorescence or the broadband channels.
  • [0522] The low signal metric acts as a hard mask because it eliminates a qualifying interrogation point from consideration by the classifier or the other masks, such as the spectral masks in step 130 of FIG. 1. The low signal metric acts as a hard mask, for example, for points that have shadowing in both BB1 and BB2.

[0523] The metrics used in this embodiment of step 128 of FIG. 1 include a low signal metric, which detects spectral data affected by obstruction artifacts such as blood, a speculum, a smoke tube, or other obstruction. This metric also identifies regions where both sets of broadband reflectance data are affected by shadow. These were combined into one low signal metric in this embodiment, since regions affected by these artifacts exhibit similar characteristics, such as low fluorescence and low broadband reflectance measurements.

[0524] FIG. 62 shows a graph 1342 depicting broadband reflectance 1344 as a function of wavelength 1346 for the BB1 channel 1348 and the BB2 channel 1350 measurements for a region of tissue where the BB1 data is affected by glare but the BB2 data is not, according to an illustrative embodiment of the invention. The glare leads to a higher value of reflectance 1344 than that of surrounding unaffected tissue. By applying the metrics listed above in step 128 of FIG. 1, it is determined that the exemplary BB1set of spectral data shown in FIG. 62 is affected by glare and is thus not suitably representative of this region of the tissue sample. Applying the metrics of step 128 also determines that the BB2 set of spectral data is potentially representative of this region of the sample (unaffected by an artifact), since it is not eliminated. One embodiment comprises using this representative data in step 132 of FIG. 1 to determine a condition of this region of the sample, for example, the state of health.

[0525] FIG. 63 shows a graph 1351 depicting broadband reflectance 1344 as a function of wavelength 1346 for the BB1 channel 1352 and the BB2 channel 1354 broadband reflectance spectral data for a region of tissue where the BB2 data is affected by shadow but the BB1 data is not, according to an illustrative embodiment of the invention. The shadow leads to a lower value of reflectance 1344 than that of surrounding unaffected tissue. By applying the metrics listed above in step 128 of FIG. 1, it is determined that the -exemplary BB2 set of spectral data shown in FIG. 63 is affected by shadow and is therefore not suitably representative of this region of the tissue sample. Applying the metrics of step 128 also leads to the determination that the BB1 set of spectral data is potentially representative of this region, of the sample, since the BB1 set of data is not eliminated. One embodiment comprises using this representative data in step 132 of FIG. 1 to determine a condition of this region of the sample, for example, the state of health.

[0526] FIG. 64 shows a graph 1358 depicting broadband reflectance 1360 as a function of wavelength 1362 for the BB1 channel 1364 and the BB2 channel 1366 measurements for a region of tissue that is obscured by blood, according to an illustrative embodiment of the invention. By applying the metrics listed above, it is determined that blood is present, and that both the BB1 and the BB2 sets of spectral data are considered unrepresentative of this region of the tissue sample.

[0527] FIG. 65 shows a graph 1367 depicting broadband reflectance 1360 as a function of wavelength 1362 for the BB1 channel 1368 and the BB2 channel 1370 measurements for a region of tissue that is unobscured, according to an illustrative embodiment of the invention. Applying this method determines that neither set of spectral data is affected by an artifact, and, therefore, either is representative of the tissue sample. One embodiment comprises using an average value 1372 of the BB1 and BB2 measurements at each wavelength to represent the region of the tissue sample in determining a condition of this region, for example, the state of health of the region, in step 132 of FIG. 1.

[0528] Application of the metrics listed above was performed using various tissue types to verify the sensitivity and specificity of the metrics. While, in one embodiment, it is undesirable to eliminate good spectral data of normal tissue, it is worse to eliminate good spectral data of diseased tissue, particularly if it is desired to use the data in the classification of the state of health of a region of tissue. The following tissue types were used in the verification: tt-132 (metaplasia by impression), tt-155 (normal by impression), tt-117 (blood), NEDpath (no evidence of disease confirmed by pathology), and cin23all (CIN ⅔ diseased tissue). Table 5 shows the number of points (regions) corresponding to each of these tissue types, the determinations from the metrics listed above for these points, and the number of points where one set of broadband reflectance spectral data were eliminated, where both sets of broadband reflectance spectral data were eliminated, and where both reflectance and fluorescence spectral data were eliminated.

[0529]

TABLE 5
Verification of Metrics
	Tissue Type
	cin23all	nedpath	tt-117	tt-132a	tt-155
Total pts.	477	919	175	5000	2016
Low Signal	2	14	126	2	0
Glare in BB1	7	30	4	122	26
Glare in BB2	9	40	9	134	16
Glare in both	3	5	1	15	5
Shadow in BB1	47	35	4	165	132
Shadow in BB2	16	37	24	359	32
One BB Removed(%)	16.6	15.5	23.4	15.6	10.2
Both BB Removed(%)	1.05%	2.07%	72.57%	0.34%	0.25%
Fl Removed(%)	0.42	1.52	72.00	0.04	0.00

[0530] For the regions (points) corresponding to CIN ⅔ diseased tissue, no broadband reflectance measurements were unnecessarily eliminated from the set using the above metrics. The points identified as being low signal were all located on the os. All points that were identified by the metric as shadow were verified as being correct, and only one point identified as glare was incorrect.

[0531] For the nedpath points (no evidence of disease), only two tissue points were unnecessarily eliminated after being misidentified as mucus. A point that was actually dark red tissue with glare was incorrectly identified as shadow in BB2. The points that were identified as glare were verified as being correct.

[0532] Out of the 175 blood points, 126 were identified as being low signal. The glare points and shadow points were accurate.

[0533] Out of the 5000 points in the metaplasia by impression group, there were no valid tissue points lost. The data set was improved by eliminating about 800 readings of points affected by either glare or shadow.

[0534] Out of the 2016 normal by impression points, no measurements were unnecessarily removed from the set.

[0535] FIG. 66 shows a graph 1374 depicting the reduction in the variability of broadband reflectance measurements 1376 of CIN ⅔-confirmed tissue produced by filtering (eliminating non-representative spectral data) using the metrics of step 128 in FIG. 1 described above, according to an illustrative embodiment of the invention. The graph 1374 depicts mean values and standard deviations of broadband reflectance spectral data before and after filtering.

[0536] FIG. 67 shows a graph 1378 depicting the reduction in the variability of broadband reflectance measurements 1376 of tissue classified as “no evidence of disease confirmed by pathology” produced by filtering using the metrics described above, according to an illustrative embodiment of the invention. The graph 1378 depicts mean values and standard deviations of broadband reflectance spectral data before and after filtering.

[0537] FIG. 68 shows a graph 1380 depicting the reduction in the variability of broadband reflectance measurements 1376 of tissue classified as “metaplasia by impression” produced by filtering using the metrics described above, according to an illustrative embodiment of the invention. The graph 1380 depicts mean values and standard deviations of broadband reflectance spectral data before and after filtering.

[0538] FIG. 69 shows a graph 1382 depicting the reduction in the variability of broadband reflectance measurements 1376 of tissue classified as “normal by impression” produced by filtering using the metrics described above, according to an illustrative embodiment of the invention. The graph 1382 depicts mean values and standard deviations of broadband reflectance spectral data before and after filtering.

[0539] FIG. 70A depicts an exemplary image of cervical tissue 1388 divided into regions for which two types of reflectance spectral data and one type of fluorescence spectral data are obtained, according to one embodiment of the invention. FIG. 70B is a representation 1398 of the regions depicted in FIG. 70A and shows the categorization of each region using the metrics in step 128 of FIG. 1. The black-highlighted sections 1390 of the image 1388 in FIG. 70A correspond to points (regions) that had both reflectance measurements eliminated by application of the embodiment method. Many of the lower points 1392, as seen in both FIGS. 70A and 70B, are in shadow because the speculum obstructs the view of one of the channels. Glare is correctly identified prominently at the upper one o'clock position 1394. Since there are blood points on the shadowed section, some are labeled blood (low signal) and others are treated as shadow.

[0540] FIG. 71A depicts an exemplary image of cervical tissue 1402 divided into regions for which two types of reflectance spectral data and one type of fluorescence spectral data are obtained, according to one embodiment of the invention. FIG. 71B is a representation 1406 of the regions depicted in FIG. 71A and shows the categorization of each region using the metrics in step 128 of FIG. 1. FIGS. 71A and 71B show an example of a cervix that has a large portion of the lower half 1404 affected by shadow. However, only one of the sets of reflectance spectral data (BB2) is affected by the shadow artifact. The BB1 reflectance spectral data is not affected by shadow. Applying the metrics above, the BB1 data are used to describe these regions, while the BB2 data are eliminated from consideration. The accuracy of tissue characterization using the reflectance measurements should be improved significantly for this patient using the arbitration metrics of step 128 of FIG. 1, since the more accurate broadband measurements will be used in later characterization steps instead of simply averaging the two broadband measurements, which would skew the measurements due to a lighting artifact.

[0541] FIG. 72A depicts an exemplary image of cervical tissue 1410 divided into regions for which two types of reflectance spectral data and one type of fluorescence spectral data are obtained, according to an illustrative embodiment of the invention. FIG. 72B is a representation 1416 of the regions depicted in FIG. 72A and shows the categorization of each region using the metrics in step 128 of FIG. 1. FIGS. 72A and 72B show an image with a portion 1412 that is shadowed and off of the cervix. Due to an obstruction from the smoke tube in the upper part of the image, there are many low signals. Even though much of the cervix is shadowed in BB1 1414, there are still some BB2 and fluorescence readings usable in later tissue classification steps.

[0542] The tissue characterization system 100 of FIG.1 combines spectral data and image data obtained by the instrument 102 to characterize states of health of regions of a tissue sample. In one embodiment, the spectral data are first motion-tracked 106, preprocessed 114, and arbitrated 128 before being combined with image data in step 132 of FIG. 1. Likewise, in one embodiment, the image data are first focused 122 and calibrated 124 before being combined with spectral data in step 132 of FIG. 1. Each of these steps are discussed in more detail herein.

[0543] FIG. 73 shows how spectral data and image data are combined in the tissue characterization system of FIG. 1, according to one embodiment. The block diagram 1420 of FIG. 73 depicts steps in processing and combining motion-tracked 106, preprocessed 114, and arbitrated 128 spectral data with focused 122, calibrated 124 image data to determine states of health of regions of a tissue sample. After preprocessing 114, spectral data from each of the interrogation points (regions) of the tissue sample are arbitrated in step 128 of FIG. 73. In the embodiment shown, a fluorescence spectrum, F, and two broadband reflectance spectra, BB1 and BB2, are used to determine one representative reflectance spectrum, BB, used along with the fluorescence spectrum, F, for each interrogation point. This is depicted in FIG. 73 as three heavy arrows representing the three spectra—BB1, BB2, and F—entering arbitration block 128 and emerging as two spectra—BB and F. Block 128 of FIG. 73 lo also applies an initial low-signal mask as a first pass at identifying obscured interrogation points, discussed previously herein.

[0544] In the embodiment of FIG. 73, the arbitrated broadband reflectance spectrum, BB, is used in the statistical classification algorithm 134, while both the broadband reflectance spectrum, BB, and the fluorescence spectrum, F, as well as the image data, are used to determine heuristic-based and/or statistics-based metrics, or “masks”, for classifying the state of health of tissue at interrogation points. Masking can be a means of identifying data that are potentially non-representative of the tissue sample. Potentially non-representative data includes data that may be affected by an artifact or obstruction such as blood, mucus, fluid, glare, or a speculum. Such data is either hard-masked or soft-masked. Hard-masking of data includes identifying interrogation points at which the data is not representative of unobscured, classifiable tissue. This results in a characterization of “Indeterminate” at such an interrogation point, and no further computations are necessary for that point. Soft-masking includes applying a weighting function or weighting factor to identified, potentially non-representative data. The weighting is taken into account during calculation of disease probability and may or may not result in an indeterminate diagnosis at the corresponding tissue region. Soft-masking provides a means of weighting spectral and/or image data according to the likelihood that the data is representative of clear, unobstructed tissue in a region of interest. In the embodiment shown in FIG. 73, both hard masks and soft masks are determined using a combination of spectral data and image data. Furthermore, the masks of FIG. 73 use spectral and image data to identify interrogation points that are not particularly of interest in the exam, such as the vaginal wall, smoke tube tissue, the os, or tissue outside the region of interest.

[0545] In addition to determining data that are potentially non-representative of regions of interest, the masks shown in FIG. 73 also include masks that determine where the data is highly indicative of necrotic tissue or disease-free (NED) tissue. It has been discovered that necrotic tissue and disease-free tissue are often more predictably determined by using a heuristic metric instead of or in combination with a statistical classifier than by using a statistical classifier alone. For example, one embodiment uses certain values from fluorescence spectra to determine necrotic regions, since fluorescence spectra can indicate the FAD/NADH component and porphyrin component of necrotic tissue. Also, an embodiment uses prominent features of fluorescence spectra indicative of normal squamous tissues to classify tissue as “NED” (no evidence of disease) in the spectral mask.

[0546] Identifying necrotic and NED regions at least partially by using heuristic metrics allows for the development of statistical classifiers 134 that concentrate on differentiating tissue less conducive to heuristic classification—for example, statistical classifiers that differentiate high grade cervical intraepithelial neoplasia (i.e. CIN ⅔) from low grade neoplasia (i.e. CIN 1) and healthy tissue.

[0547] In FIG. 73, step 130 uses the arbitrated spectra, BB and F, to determine four spectral masks—NED_spec (no evidence of disease), Necrosis_spec, [CE]_spec (cervical edge/vaginal wall), and [MU]_spec (mucus/fluid). The focused, calibrated video data is used to determine nine image masks—Glare_vid, Mucus_vid, Blood_vid, OS_vid, [ROI]_vid (region of interest), [ST]_vid (smoke tube), [SP]_vid (speculum), [VW]_vid (vaginal wall), and [FL]_vid (fluid and foam). Step 1422 of FIG. 73 combines these masks to produce a hard “indeterminate” mask, a soft “indeterminate” mask, a mask identifying necrotic regions, and a mask identifying healthy (NED) regions. In the embodiment of FIG. 73, steps 1424 and 1426 apply the necrotic mask and hard “indeterminate” mask, respectively, prior to using the broadband spectral data in the statistical classifiers 134, while steps 1428 and 1430 apply the soft “indeterminate” mask and the NED mask after the statistical classification step 134.

[0548] The embodiment shown in FIG. 73 can classify each interrogation point in step 1432 as necrotic, CIN ⅔, NED, or Indeterminate. There may be some post-classification processing in step 1434, for example, for interrogation points having a valid fluorescence signal but having both broadband signals, BB1 and BB2, eliminated by application of the arbitration metrics in step 128. The embodiment in FIG. 73 then uses the final result to create a disease display overlay of a reference image of the tissue sample in step 138. Each of the masking and classification steps summarized above are discussed in more detail herein.