A segmentation method of left ventricle in cardiac magnetic resonance images based on improved snake model

被引：0
|
作者：
Zhu, Min [1 ]
Zhang, Weixue [1 ]
Qu, Quanmin [2 ]
Li, Mengying [1 ]
Gao, Lifeng [1 ]
机构：
[1] College of Computer Sci., Sichuan Univ., Chengdu
[2] School of Info. Sci. and Eng., Northeastern Univ., Shenyang
来源：
Sichuan Daxue Xuebao (Gongcheng Kexue Ban)/Journal of Sichuan University (Engineering Science Edition) | 2015年 / 47卷 / 02期
关键词：
Cardiac magnetic resonance images; Extended neighborhood Sigmoid gradient vector flow; Image segmentation; Shape constraint; Snake model;
D O I：
10.15961/j.jsuese.2015.02.012
中图分类号：
学科分类号：
摘要：
A novel method for segmenting cardiac magnetic resonance images based on Snake model was proposed. An external force called extended neighborhood Sigmoid gradient vector flow ENSGVF was presented as the improvement of gradient vector flow (GVF) for Snake which has a good performance on deep and narrow concavity convergence, capture range and weak edge preserving. In terms of the segmentation of endocardium, and considering that the left ventricle is roughly a circle, a circle shape constraint was adopted on the basis of ENSGVF Snake models, which can eliminate the unexpected local minimum caused by image inhomogeneity and papillary muscle. For the segmentation of epicardium, making full use of the segmentation result of endocardium, a new external force field and a new shape constraint were constructed to achieve automatic precise segmentation. The experimental results showed that the proposed method can address the challenges of lake of edge inhomogeneity, image inhomogeneity, effect of papillary muscle, and improves the rate of accuracy. ©, 2015, Editorial Department of Journal of Sichuan University. All right reserved.
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页码：82 / 88
页数：6
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