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C. Platero, M. C. Tobar, J. Sanguino, J. M. Poncela and O. Velasco, “Level Set Segmentation with Shape and Appearance Models Using Affine Moment Descriptors,” Pattern Recognition and Image Analysis, Vol. 6669,2011, pp. 109-116. doi:10.1007/978-3-642-21257-4_14

has been cited by the following article:

• TITLE: Liver Segmentation from CT Image Using Fuzzy Clustering and Level Set

  AUTHORS: Xuechen Li, Suhuai Luo, Jiaming Li

  KEYWORDS: Liver Segmentation; Fuzzy c-Mean Clustering; Level Set

  JOURNAL NAME: Journal of Signal and Information Processing, Vol.4 No.3B, October 16, 2013

  ABSTRACT: This paper presents a fully automatic segmentation method of liver CT scans using fuzzy c-mean clustering and level set. First, the contrast of original image is enhanced to make boundaries clearer; second, a spatial fuzzy c-mean clustering combining with anatomical prior knowledge is employed to extract liver region automatically; thirdly, a distance regularized level set is used for refinement; finally, morphological operations are used as post-processing. The experiment result shows that the method can achieve high accuracy (0.9986) and specificity (0.9989). Comparing with standard level set method, our method is more effective in dealing with over-segmentation problem.