Segmentation of coronary arteriograms by iterative ternary classification.
A segmentation algorithm for extracting arterial structures in coronary angiograms is presented. The algorithm mimics the process of interactive interpretation in human vision by iteratively implementing a ternary classification and learning process. Two gray-scale thresholds are computed to define three pixel classes: artery, background, and undecided. Then, two new thresholds for undecided pixels are computed using statistics conditioned upon the current classification. The threshold adaptation is governed by a learning algorithm based on the line and consistency measurements around each pixel. The process converges and results in a binary image. The performance of this algorithm on human coronary arteriograms was compared qualitatively to that of a relaxation algorithm and of a scattering based algorithm. Quantitative comparison was also made possible with computer generated images, which were obtained with the help of a model of the imaging chain and a process of interactive visualization of the modeled data. The iterative ternary classifier showed the best performance over a broad range of image quality. The study also demonstrated the use of visualization and user interaction in model building and algorithm development.
Kottke
D P
DP
Department of Electrical Engineering, College of Engineering, University of Rhode Island, Kingston 02881.
Sun
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Y
D000465:Algorithms; D002932:Cineangiography / Q000379:methods; D017023:Coronary Angiography; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008955:Models, Cardiovascular