Analyzing the Structures of Coronary Artery Trees in Angiographic Images Based on Fuzzy Recognition Algorithm

The qualitative and quantitative description of coronary artery depends largely on inferring the artery tree structure in the angiographics. In this paper, an algorithm of Multi-feature based fuzzy recognition is proposed to infer vessel structure in the angiographics. In the implementation, the initial vessel features are attained by preprocessing the original image, and then a circle-detector is used to scan and calculate the local multi-feature metrics along the vessel path. After defining the fuzzy subset of the multi-feature metrics and its membership degree function, a fuzzy operator is constructed to infer the vessel structures, i.e., the distal ends, segments, bifurcations and crossovers of the artery tree. The algorithms perform well in a simulated phantom, and the ratio of correct identification of structure in the clinical angiographics reaches on average to 92.88%.