Chinese Journal of Computers   Full Text
Title	Analyzing the Structures of Coronary Artery Trees in Angiogram Images Based on Fuzzy Recognition Algorithm
Authors	JIANG Gui-Ping1) ZHOU Shou-Jun2)
Address	1)(School of Biomedical Engineering, Southern Medical University, Guanzhou 510515) 2)(Department of Opto-Electronic Engineering, Beijing Institute of Technology， Beijing 100081)
Year	2008
Issue	No.1(170—175)
Abstract &   Background	Abstract The qualitative and quantitative description of coronary artery depends largely on inferring the artery tree structure in the angiograms. In this paper, an algorithm of Multi-feature based fuzzy recognition is proposed to infer vessel structure in the angiograms. 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 multi-feature metrics along the vessel path. After defining the membership degree of the multi-feature metrics, 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 ratios of structure identification reach on average to 92.60% in the clinical angiograms. keywords coronary artery angiogram; X-Ray Angiogram(XRA); vascular structure inferring; fuzzy recognition algorithm background This research is supported by the National Basic Research Program of China (973 Program) under grant No.2003CB716100 called MIP973 and National Science Foundation of China under grant No.60772120. The grants aim to establish a set of fundamental theory and algorithms for medical image processing and electrophysiological signal processing, which are one of the most essential technical supports for the development of medical imaging equipments and electrophysiological devices. The MIP973 is divided into 6 subprojects. This paper is an outcome of the image analysis group, one direction of the fifth subproject-Intelligent Analysis and 3D Reconstruction of DSA. The angiography technique has been applied in clinic for more than 60 years, it is the critical method for non-invasive and interventional surgery navigation for cardiac cerebral vascular disease. But the 2D angiography image makes diagnosis and therapy very difficult, especially in spacial location of ducts. Therefore, true 3D reconstruction of vessels is the key point to solve the problem, which should includes automatic segmentation, recognition and reconstruction of vascular. This paper is the first step towards the goal.