Computed tomography angiography (CTA) is one of the most widely used imaging techniques to diagnose coronary artery disease. However

the similarities of the gray scale distributions of both coronary arteries and surrounding tissues make the recognition of vascular structures difficult. This study proposes a coronary artery tracking method based on direction clustering. Vessel structures are enhanced by integrating the closing operation and adaptive grayscale stretching. A spherical operator is proposed for the detection of vascular boundaries. The operator is constructed by placing direction rays at the center of the extraction points. The intensity difference ratios of adjacent sampling points are extracted based on the intensity distribution on the rays. Vascular boundaries are then extracted and refined through convex hull optimization. Finally

hierarchical clustering is employed to extract the direction vector of the vascular structure. demonstrate that the proposed method can accurately extract the coronary artery from CTA images. The extraction accuracy can be reached to 0.39 mm. The designed spherical operator can be well adaptive to different kinds of 3D vessels. And the hierarchical clustering method that is used for the director vector classification don't need the training. The proposed method has high automaticity and accuracy.