Domain-specific approach for segmentation of nucleus-cytoplasm in bone cancer histopathology for malignancy analysis
by P.J. Antony; B.S. Vandana; Sathyavathi R. Alva
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 27, No. 4, 2018

Abstract: Bone cancer is prevalent and early detection of disease is need of the day. Most of the computer-assisted diagnosis tools of research pertain to various other parts of the body because of its simple tissue structure. Proposed research focuses on specific Ewing sarcoma stained with Haematoxylin and Eosin (H&E) data set wherein nucleus and cytoplasm features are extracted to define cancer. Relating to this two domain-specific segmentation algorithms are proposed: (1) model-based clustering, (2) gradient-based watershed segmentation methods are applied to extract nucleus and cytoplasm. H&E component of image are separated to improve quality of segmentation. Segmentation algorithms applied in parallel on H&E images to improve time factor. Morphological and texture features extracted from segmented images are used to train Support Vector Machine (SVM) for classification of malignancy.30 images of varying features are selected to train SVM classifier and 30 images are used for validation and the accuracy obtained is 94.5%.

Online publication date: Tue, 28-Aug-2018

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