Computer Aided Segmentation of Blockages in Coronary Heart Images Using Canfis Classifier

Coronary vessel blockage segmentation is the fundamental component which extracts significant features from angiogram images to detect heart disease. This paper proposes an automated method of blockage segmentation from coronary angiogram images using coactive adaptive neuro fuzzy inference system (CANFIS) classifier. The proposed method consists of preprocessing, feature extraction and classification. The vessels in the coronary image are enhanced using preprocessing technique and then features are extracted from these images which are given to the CANFIS classifier. This classifier classifies the given test coronary image into either normal or abnormal. Further, the blockage is detected and segmented if the proposed system classifies the test image as abnormal. The proposed method achieves 99.76% sensitivity, 99.9% specificity and 99.9% accuracy for blockage vessel pixel detection.

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