A coronary artery segmentation method based on region growing with variable sector search area

BACKGROUND: Coronary artery image segmentation is an important auxiliary method for coronary artery disease diagnosis. OBJECTIVE: The classical region growing algorithms, which only consider the intensity of pixels, are noise-sensitive and require manual interaction. To this end, recent methods simultaneously consider the intensity of pixels and multi-scale analysis with the region growing. Nevertheless, these methods are not fully optimized and they suffer from the drawbacks of over- or under-segmentation in many cases. METHODS: In this paper, we propose a region growing based coronary artery segmentation method. Different from the existing methods, the variable sector search area is considered in the region growing technique. A growing rule is proposed to segment the vessel, which combines the Hessian vector and the region growing with the variable sector search area. To further improve the quality of segmentation, we propose an optimization of removing some small disconnected regions. RESULTS: Our proposed method can search more branches while segmenting the vessel, even the small ones. It keeps an acceptable performance when dealing with stenosis and large curvature of blood vessels. CONCLUSIONS: Quantitative evaluations are conducted on coronary angiography and the results show that the proposed method achieves a higher DSC ratio and a more reliable sensitivity ratio.