Owing to the complexity and randomness of CTA images in the clinic

clinicians are required to manually segment the coronary artery to diagnose a patient suffering from coronary artery diseases. How to segment the coronary artery quickly

accurately

and automatically is important for diagnostic efficiency. According to the features of dual-source CT image

a three-dimensional segmentation algorithm for cardiac coronary artery is investigated. To solve the difficulty of single region or boundary-based active contour model

the segmentation method of active contour model-based vessel prior shape is proposed. First

the improved fuzzy C-means algorithm is used for the initial coarse segmentation of the region of interest (ROI) in cardiac CT images to extract the ROI. The coarse segmentation result is applied to initialize the C-V model level set automatically and estimate the controlling parameters for level set evolution. Next

the 3D heart volume data are used to obtain multiscale gradient vector information and determine the structure of the boundary-based energy function. Then

the vascular structures within the 3D ROI volume are enhanced using the Hessian matrix-based multiscale vascular function. After vascular filtering

the vessel's prior shape information can be obtained to construct the region-based energy function. Finally

the boundary- and region-based energy functions are fused to construct the hybrid energy function

and the variation principle and level set method are used to derive the level set evolution equation for coronary vessel segmentation. The gray scales of vessel images are uneven

and the area of the terminal vessel is smaller than the others; thus

the aforementioned algorithm will be divided into several small-scale subareas of the vessels for the evolution of the contour. The vessel prior information and gradient information are fused to segment accurately the coronary artery from the image with uneven distribution of the gray and contrast agents

better than the conventional vessel segmentation method. Furthermore

good segmentation effect can further be obtained even for a small vessel structure. Experimental results show that the proposed method is only dependent on the initial contour to extract 3D vascular images effectively. Several groups of heart volumes are tested to evaluate its performance in this study. The results show that active contour model-based vessel prior shape can accurately segment the complete coronary structures with a simple human interaction. The method has good accuracy and superiority in terms of the automatic segmentation of a dual-source CT coronary artery image.