Segmentation of Multiple Nuclei from Non-overlapping Immuno-histochemically Stained Histological Hepatic Images.

In this paper, we describe an algorithm for accurately segmenting multiple nudfclei from clumps of non-overlapping immuno-histochemically stained histological hepatic (liver) images. This problem is notoriously difficult because of the degree of presence of stains among the multi-nucleated cells, the poor contrast of cell cytoplasm, and the presence of mucus, blood, and inflammatory cells in the images. Hepatocellular carcinoma, characterized by cellular and nuclear enlargement, nuclear pleomorphism, and multi-nucleation, poses a prominent threat.

Nuclei detection in hepatocellular carcinoma and dysplastic liver nodules in histopathology images using bootstrap regression.

Hepatocellular carcinoma (HCC) is the most common primary malignant neoplasm of the liver representing the fifth most common malignancy worldwide. This tumor is more common in men than women, with a ratio of 2.7:1.