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| Variational level set medical image segmentation based on kernel fuzzy clustering |
| School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China |
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Abstract The existing variational level set without reinitialization model proposed by Li Chunming is less sensitive to images of similar internal pixel gray value with bad edge separation, and the segmentation results are not satisfying. To solve the problems, the variational level set medical image segmentation method was proposed based on kernel fuzzy clustering. The original image was transformed by kernel fuzzy Cmeans clustering， and the clustering results were introduced into the initial level set function． The improved edge indicator function was brought into the Li model to achieve the ultimate image segmentation. The experiments were conducted on MR images of human brain and shoulder with the proposed method, and the results were objectively evaluated with the maximum Shannon entropy. The experimental results show that the maximum Shannon entropy of the proposed method is higher than that of Li model method to a certain extent, and the proposed method contains less elapsed time and less iteration times at the same time. The proposed method has good segmentation quality and strong adaptability without reinitialization.
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Received: 07 May 2014
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2014, Vol. 35 
