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Microscopic images from the biopsy samples of cervical cancer, the current"gold standard" for histopathology analysis, are found to be segregated intodiffering classes in their correlation properties. Correlation domains clearlyindicate increasing cellular clustering in different grades of pre-cancer ascompared to their normal counterparts. This trend manifests in theprobabilities of pixel value distribution of the corresponding tissue images.Gradual changes in epithelium cell density are reflected well through thephysically realizable extinction coefficients. Robust statistical parameters inthe form of moments, characterizing these distributions are shown tounambiguously distinguish tissue types. These parameters can effectivelyimprove the diagnosis and classify quantitatively normal and the precanceroustissue sections with a very high degree of sensitivity and specificity.

Statistical characterization of tissue images for detection and classification of cervical precancers