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Computational and Mathematical Methods in Medicine
Volume 2014, Article ID 914028, 8 pages
Research Article

A Novel Adaptive Level Set Segmentation Method

1The 175 Hospital, Southeast Hospital of Xiamen University, Zhangzhou, Fujian 363000, China
2Zhengzhou University of Light Industry, Zhengzhou, Henan 450002, China
3Southern Medical University, Guangzhou, Guangdong 510515, China

Received 12 May 2014; Revised 20 August 2014; Accepted 20 August 2014; Published 1 September 2014

Academic Editor: Michele Migliore

Copyright © 2014 Yazhong Lin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The adaptive distance preserving level set (ADPLS) method is fast and not dependent on the initial contour for the segmentation of images with intensity inhomogeneity, but it often leads to segmentation with compromised accuracy. And the local binary fitting model (LBF) method can achieve segmentation with higher accuracy but with low speed and sensitivity to initial contour placements. In this paper, a novel and adaptive fusing level set method has been presented to combine the desirable properties of these two methods, respectively. In the proposed method, the weights of the ADPLS and LBF are automatically adjusted according to the spatial information of the image. Experimental results show that the comprehensive performance indicators, such as accuracy, speed, and stability, can be significantly improved by using this improved method.