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Automated image examination of histopathology specimens helps for early detection and better characterization of liver cirrhosis. A new method for the segmentation of overlapping nuclei and non-nuclei cell is evaluated, which is named as Online Region based-Active Contour Model (OR-ACM). In this study, a semantic feature extraction (combination of Histogram of Oriented Gradients (HOG) (descriptor level feature) and colour moments (low-level features)) is performed on segmented nuclei cells to evaluate the growth of Endothelial Cells (ECs). It is characterized into two levels such as micro and macro cells that is classified by utilizing Multi-Support Vector Machine (M-SVM). Experimental outcome shows that the proposed approach improved accuracy in liver cirrhosis prediction up to 3% compared to the existing methods: Adaptively Regularized Kernel-Based Fuzzy C-Means (ARKFCM) Euclidean distance, ARKFCMEarth movers.

Detection of Macro and Micro Nodule Using Online Region Based-Active Contour Model in Histopathological Liver Cirrhosis