Abstract: The invention provides methods for determining the probability that a given region of a tissue sample contains tissue of a given category, such as CIN 1 (cervical intraepithelial neoplasia, grade 1), CIN 2/3 (cervical intraepithelial neoplasia grades 2 and/or 3), normal squamous, normal columnar, and metaplasia, for example. The invention provides increased diagnostic accuracy by combining a plurality of statistical classification techniques. Furthermore, in one embodiment, the invention comprises combining one or more statistical techniques with one or more non-statistical classification techniques.

Abstract: A method and system for normalizing optical spectra using a non-uniform segment normalization. A spectrum is obtained and is represented as a function of wavelength as an amplitude at each of a plurality of wavelengths. At least one segment of the spectrum is selected, each selected segment being bounded by an upper wavelength and a lower wavelength. A normalization factor is computed as the sum of the areas for each of the selected segments. The spectrum is normalized by dividing at least one amplitude of the spectrum by the normalization factor. Segments can be selected with different wavelength ranges, that is, segments can be non-uniform. Test specimens can be categorized based on an analysis of normalized spectra. In particular, the specimen to be tested can be human cervical tissue, and the state of health of the tissue can be determined.

Abstract: The invention provides methods for processing tissue-derived optical data for use in a classification algorithm. Methods of the invention comprise application of image masks for automatically identifying ambiguous or unclassifiable optical data. The optical data may comprise, for example, spectral data and/or acetowhitening kinetic data used in a tissue classification scheme. The invention improves the accuracy of tissue classification, in part, by properly identifying and accounting for image data from tissue regions that are affected by an obstruction and/or regions that lie outside a diagnostic zone of interest.

Abstract: The invention provides a system and methods for in-situ identification of one or more regions of tissue at which there is a likelihood of disease. The invention generally relates to methods and devices for acquiring, analyzing, processing, and displaying optical data and diagnostic results from a patient sample. For example, methods of the invention comprise obtaining spectral and visual data from a patient sample, calibrating the data, compensating for sample motion, arbitrating between redundant data sets, identifying potentially non-representative data, analyzing the data, and displaying the diagnostic results. The invention provides the option of real-time data processing and diagnosis.

Abstract: The invention provides methods for determining the probability that a given region of a tissue sample contains tissue of a given category, such as CIN 1 (cervical intraepithelial neoplasia, grade 1), CIN 2/3 (cervical intraepithelial neoplasia grades 2 and/or 3), normal squamous, normal columnar, and metaplasia, for example. The invention provides increased diagnostic accuracy by combining a plurality of statistical classification techniques. Furthermore, in one embodiment, the invention comprises combining one or more statistical techniques with one or more non-statistical classification techniques.

Abstract: A system and method for classifying specimens using a plurality of spectral data types. Spectra are recorded as amplitudes at a series of discrete wavelengths. Pluralities of reference spectra are recorded for specimens having known conditions. A spectrum of a first type is observed for a test specimen. The specimen is characterized as to a first known condition. In the event that the specimen does not exhibit the first known condition, a spectrum of a second type is observed and analyzed to determine which of a plurality of conditions is to be ascribed to the test specimen. In some embodiments, the test specimen can comprise human cervical tissue, and the known conditions can include normal health, metaplasia, CIN I and CIN II/III.

Abstract: A method and system for normalizing optical spectra using a non-uniform segment normalization. A spectrum is obtained and is represented as a function of wavelength as an amplitude at each of a plurality of wavelengths. At least one segment of the spectrum is selected, each selected segment being bounded by an upper wavelength and a lower wavelength. A normalization factor is computed as the sum of the areas for each of the selected segments. The spectrum is normalized by dividing at least one amplitude of the spectrum by the normalization factor. Segments can be selected with different wavelength ranges, that is, segments can be non-uniform. Test specimens can be categorized based on an analysis of normalized spectra. In particular, the specimen to be tested can be human cervical tissue, and the state of health of the tissue can be determined.

Abstract: A system and method for classifying tissue by application of discriminant analysis to spectral data. Spectra are recorded as amplitudes at a series of discrete wavelengths. Pluralities of reference spectra are recorded for specimens having known conditions. The reference spectra are subjected to discriminant analysis to determine wavelength regions of interest for the analysis. A plurality of amplitudes are selected for the analysis, and are plotted in an N-dimensional space. For each plurality of reference spectra corresponding to a specific known condition, a characteristic point is determined and plotted, the characteristic point representative of the known condition. A test spectrum is recorded from a test specimen, and the plurality of amplitudes corresponding in wavelength to the wavelength regions of interest are selected. A characteristic point in N-dimensional space is determined for the test spectrum.

Abstract: A system and method for classifying specimens using a plurality of spectral data types. Spectra are recorded as amplitudes at a series of discrete wavelengths. Pluralities of reference spectra are recorded for specimens having known conditions. A spectrum of a first type is observed for a test specimen. The specimen is characterized as to a first known condition. In the event that the specimen does not exhibit the first known condition, a spectrum of a second type is observed and analyzed to determine which of a plurality of conditions is to be ascribed to the test specimen. In some embodiments, the test specimen can comprise human cervical tissue, and the known conditions can include normal health, metaplasia, CIN I and CIN II/II.

Abstract: A system and method for classifying specimens using a plurality of spectral data types. Spectra are recorded as amplitudes at a series of discrete wavelengths. Pluralities of reference spectra are recorded for specimens having known conditions. A spectrum of a first type is observed for a test specimen. The specimen is characterized as to a first known condition. In the event that the specimen does not exhibit the first known condition, a spectrum of a second type is observed and analyzed to determine which of a plurality of conditions is to be ascribed to the test specimen. In some embodiments, the test specimen can comprise human cervical tissue, and the known conditions can include normal health, metaplasia, CIN I and CIN II/II.