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PARAMETER-FREE MULTI-LEVEL FUZZY C-MEANS CLUSTERING FOR UNSUPERVISED STRUCTURE DETECTION IN HISTOLOGICAL IMAGES

Histopathologic assessment remains one of the most critical steps in the diagnosis and subsequent treatment of most types of cancer. Recently, the improvements in imaging techniques have led to the discovery of virtual histological slides; these are high resolution image files with a resolution of 0.25 um/pixel. These images allow researchers to investigate cancer cell morphologies at the finest level, such as abnormal cells in stained tumor regions at high magnification. In this study, the goal is to separate regions representing cell nuclei from the stroma and fat like regions, and two layer of microvessel. More specifically, automatic isolation and evaluation of potentially malignant cells (region of interest) from histological slides for cancer detection are investigated. We also applied same procedure to angiogenesis detection in liver tissue. Technically, region of interest (ROI) corresponds to the delineated region of malignancy or pericytes in each subimage. We propose a novel approach for detection of ROI. For automated region of interest extraction, Fuzzy C-Means (FCM) clustering algorithm is used and a framework for Multi-Level FCM clustering is developed. Our early framework is applied to the histological slides and yielded high promising results.