Histopathology Image Analysis and Classification for Cancer Detection Using 2D Autoregressive Model

Abstract

In traditional methods of cancer diagnosis using clinical pathology, pathologists inspects biopsy samples and make diagnostic inferences. These diagnostics are based on cell morphology and tissue distribution which represents randomness in growth and/or in placement. These methods are highly subjective and sometimes lead to incorrect diagnosis. But computer assisted diagnostics (CAD) enables objective judgment that is based on huge collected database. This paper presents the use of 2D autoregressive (AR) model in automated cancer diagnosis based on histopathology. This work emphasizes the contribution of 2D autoregressive models for analysis and classification of histopathological images. Autoregressive model parameter represents feature set of histopathological image of biopsy samples removed from patients. These features are further used for analysis, synthesis and classification. Yule walker Least Square (LS) method has used for parameter estimation. The test statistics for choice of proper order of model has also been suggested in paper. It has been inferred that the proper neighborhood for a given class sample images is unique and solely depends on the properties of samples under consideration. AR parameters provide features for classification of sample in three classes like benign tissue, healthy tissue and malignant tissue.
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