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J. Kim, J. W. Fisher, A. Yezzi, M. Cetin and A. S. Willsky, “A Nonparametric Statistical Method for Image Segmentation Using Information Theory and Curve Evolution,” IEEE Transactions on Image Processing, Vol. 14, No. 10, 2005, pp. 1486-1502. doi:10.1109/TIP.2005.854442

has been cited by the following article:

• TITLE: Improve the Nonparametric Image Segmentation with Narrowband Levelset and Fast Gauss Transformation

  AUTHORS: Murong Jiang, Yinghao Zhong, Xin Wang, Xiaotong Huang, Ruilin Guo

  KEYWORDS: Nonparametric Image Segmentation; Mutual Information; Narrowband Levelset; Fast Gauss Transformation

  JOURNAL NAME: Applied Mathematics, Vol.3 No.11A, November 27, 2012

  ABSTRACT: Nonparametric method based on the mutual information is an efficient technique for the image segmentation. In this method, the image is divided into the internal and external labeled regions, and the segmentation problem constrained by the total length of the region boundaries will be changed into the maximization of the mutual information between the region labels and the image pixel intensities. The maximization problem can be solved by deriving the associated gradient flows and the curve evolutions. One of the advantages for this method does not need to choose the segmentation parameter; another is not sensitive to the noise. By employing the narrowband levelset and Fast Gauss Transformation, the computation time is reduced clearly and the algorithm efficiency is greatly improved.