Automatic Segmentation of Coronary Angiograms Based on Probabilistic Tracking

This paper presents a novel tracking method for automatic segmentation of coronary artery tree in the X-ray angiographic images, based on probabilitistic vessel tracking and structure pattern inferring. The method is composed of two main steps, namely preprocessing, and tracking. In the preprocessing step, multiscale Gabor filtering and Hessian matrix analysis are used to enhance and extract vessels from the original angiographic image, leading to a vessel feature map as well as a vessel direction map. In the tracking step, a probabilistic tracking operator is proposed to extract vessel segments or branches, together with a detector to identify vessel structure. The identified structure pattern is used to control the tracking process. By appropriate integration of these advanced preprocessing and tracking steps, the algorithm is able to extract both vessel axis-lines and edge points, and to measure the arterial diameters in various complicated cases. The experimental results were satisfying.