| Autor: |
Tafavogh, Siamak, Kennedy, Paul J., Catchpoole, Daniel R. |
| Zdroj: |
2012 International Joint Conference on Neural Networks (IJCNN); 1/ 1/2012, p1-8, 8p |
| Abstrakt: |
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. [ABSTRACT FROM PUBLISHER] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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