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• W2055491010 abstract "A Computer Aided Diagnosis (CAD) system is developed to determine cellularity status of a tumor. The system helps pathologists to distinguish a tumor with cell proliferation from normal tumors. The developed CAD system implements a hybrid segmentation method to identify and extract the morphological features that are used by pathologists for determining cellularity status of tumor. Adaptive Mean Shift (AMS) clustering as a non-parametric technique is integrated with Color Template Matching (CTM) to construct segmentation approach. We used Expectation Maximization (EM) clustering as a parametric technique for the sake of comparison with our proposed approach. The output of our proposed system and EM are validated by two pathologists as ground truth. The result of our developed system is quite close to the decision of pathologists, and it significantly outperforms EM in terms of accuracy." @default.
• W2055491010 created "2016-06-24" @default.
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• W2055491010 date "2012-06-01" @default.
• W2055491010 modified "2023-09-23" @default.
• W2055491010 title "Determining cellularity status of tumors based on histopathology using hybrid image segmentation" @default.
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• W2055491010 doi "https://doi.org/10.1109/ijcnn.2012.6252768" @default.
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