Medispectra, Incorporated

Mediaspectra, Inc.

Peter J Costa

Robert Nordstrom

Kwong Hui

Ross Flewelling

Kevin Schomacker

Howard B Kaufman

Jean-Pierre Schott

Stephen T Sum

Bret F Draayer

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.

Methods and apparatus for characterization of tissue samples

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.

System for normalizing spectra

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. The distance of the characteristic point of the test spectrum from each of the plurality of characteristic points representative of known conditions is determined. The test specimen is assigned the condition corresponding to the characteristic point of a plurality of reference spectra, based on a distance relationship with at least two distances, provided that at least one distance is less than a pre-determined maximum distance. 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.

Spectral data classification of samples

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.

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Hudson, MA, United States of America

Peter J Costa