Abstract
In the present world, medical diagnosis systems depend a lot on medical imaging and digital nosology. The digital nosology is leading to computer-aided diagnosis to facilitate the rapid and accurate screening of specimens. In this work, we present a method based on object shaped kernel for histopathology image segmentation to identify and analyze the cell nuclei region. The proposed segmentation technique is a multi-stage procedure, which includes contrast enhancement of the image, nuclei region extraction, nucleus centroid marking, nuclei area refinement and, complex nuclei separation. The performance of proposed method is measured using three standard H&E stained histopathology image datasets and a proposed dataset. The simulation results of the segmentation schemes are evaluated using the F1-score, Hausdorff distance and Jaccard index. The proposed system also introduced a novel performance measure as cumulative factor. The superiority of proposed segmentation system is verified in terms of cumulative factor in comparison to various the state of art methods.
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