A Novel Automatic Coronary Artery Segmentation Method Based on Region Growing with Annular and Spherical Sector Partition

Coronary artery segmentation based on the Computerized Tomography Angiography images is an important subject in the field of vascular medical imaging. In this paper, a novel automatic coronary artery segmentation method based on region growing with annular and spherical sector partition is proposed to improve search efficiency and quality of blood vessel segmentation. In our proposed method, the region is divided into a series of annular sectors based on the characteristics of vascular shape in the two-dimensional image, while the space is divided into spherical sectors according to the shape and tendency of the vessel in the three-dimensional image. The proposed method has been tested by 6 groups of data set, the efficiency and quality of the automatic segmentation has been significantly improved. Not only can the coronary artery and its adhesion tissue be successfully separated, but also the coronary arteries and their small branches can be detected. Furthermore, compared to the multiscale region growing method, our proposed method is able to search more branches. Also it is able to achieve higher total coverage ratio and Dice Similarity Coefficient ratio.