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.
Department of Electrical Engineering, College of Engineering, University of Rhode Island, Kingston 02881.
D P
DP
Kottke
Y
Y
Sun
D000465:Algorithms; D002932:Cineangiography / Q000379:methods; D017023:Coronary Angiography; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008955:Models, Cardiovascular