Graph Cuts and Shape Statistics Based Cardiac MR Image Segmentation Using Active Contours Model
LIU Fu-Chang1, ZHU Jin1, YANG Ya-Fang2, HENG Pheng Ann3, XIA De-Shen1
1.School of Computer Science and Technology, Nanjing University of Science and Technology,Nanjing 210094 2.The Second Affiliated Hospital, Nanjing Medical University, Nanjing 210011 3.Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong
Abstract To analyze heart function effectively, it is necessary to segment the left and right ventricles precisely. In cardiac MR images, the weak edges, broken boundaries, region inhomogeneity and noises cause difficulties in segmenting the contours of left and right ventricle precisely. In this paper, the training samples are aligned and analyzed, and the allowable shape space of the left and right ventricles is constructed. An active contours model based on graph cuts and shape statistics is proposed for segmentation of cardiac MR images. It uses graph cuts based active contours (GCBAC) to convert the image segmentation into the globally optimal partition after transforming the image into a graph. Next, GCBAC uses graph cuts to iteratively deform the contour. Consequently, it has a large capture range. Then, the shape statistics is introduced into GCBAC. The introduction of shape statistics prevents the deformation curve form leaking out of actual boundaries. Experimental results demonstrate the proposed method achieves a higher segmentation precision and a better stability than other approaches and it provides a feasible way for clinical applications.
LIU Fu-Chang,ZHU Jin,YANG Ya-Fang等. Graph Cuts and Shape Statistics Based Cardiac MR Image Segmentation Using Active Contours Model[J]. , 2009, 22(2): 275-281.
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