A level set method for convexity preserving segmentation of cardiac left ventricle
Cong Yang, Xue Shi, Donglan Yao, Chunming Li · 2017
In this paper, a level set method is proposed for the segmentation of Left Ventricle (LV) from short-axis cardiac magnetic resonance images. According to the anatomical knowledge of LV, we first propose a convexity preserving mechanism to keep the shape of the evolving contour convex during the curve evolution, and thereby improves the segmentation accuracy. Then, the mechanism is incorporated into two-layer level set method to delineate endocardial and epicardial boundaries simultaneously. The proposed method has been quantitatively validated on a public dataset, and experimental results and comparisons with other methods demonstrate the superior performance of our method. Furthermore, such a generally constrained convexity-preserving level set method can be useful in many other potential applications, as validated by experiments.