Multiphase Image Segmentation from a Statistical Framework
The study is to investigate a new representation of a partition of an image domain into a number of regions using level set method derived from a statistical framework. The proposed model is composed of evolving simple closed planar curves by a region-bas
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Multiphase Image Segmentation from a Statistical Framework Jiangxiong Fang, Huaxiang Liu, Juzhi Deng, Yulin Gong, Haning Xu and Jun Liu
Abstract The study is to investigate a new representation of a partition of an image domain into a number of regions using level set method derived from a statistical framework. The proposed model is composed of evolving simple closed planar curves by a region-based force determined by maximizing the posterior image densities over all possible partitions of the image plane containing two terms: a Bayesian term based on the prior probability, a regularity term adopted to avoid the generation of excessively irregular and small segmented regions. This formulation leads to a system of coupled curve evolution equations, which is easily amenable to a level set implementation, and an unambiguous segmentation because the evolving regions form a partition of the image domain at all time during curve evolution. Given these advantages, the proposed method can get good performance and experiments show promising segmentation results. Keywords Multiphase image segmentation
Level set Statistical approach
37.1 Introduction Image segmentation is a fundamental problem in image processing and computer vision. Its goal is to partition a given image into several parts in each of which the intensity is homogeneous. It plays an important role in numerous useful applications, e.g., SAR image processing, [1] biomedical image processing, [2] scene J. Fang (&) H. Liu J. Deng Y. Gong H. Xu J. Liu Department of Nuclear Engineering and Physical Geography, East China Institute of Technology, Nanchang 330013, China e-mail: [email protected] J. Fang Jiangxi Province Key Lab for Digital Land, Fuzhou 344000, China
A. A. Farag et al. (eds.), Proceedings of the 3rd International Conference on Multimedia Technology (ICMT 2013), Lecture Notes in Electrical Engineering 278, DOI: 10.1007/978-3-642-41407-7_37, Springer-Verlag Berlin Heidelberg 2014
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interpretation, and [3] video image analysis; [4] since it facilitates the extraction of information and interpretation of image contents. Over these decades, many approaches have been developed to solve the image segmentation problem. Researchers have also done great efforts to improve the performance of the image segmentation algorithms. However, it is still a difficult problem to solve for complicated images. In recent years, level set method [5] is the most important and successful method for image segmentation. Chan-Vese (C–V) model [6] is one of the most popular active contour models based on Mumford-Shah segmentation formulas. With no reliance on the gradient to stop the propagation process, the model becomes an energy minimizing segmentation which can be seen as a particular case of the minimal partition problem. Later, Vese, and Chan proposed a multiphase level set framework [8] represented by multiple level set functions. But the interiors of two or more curves may overlap, leading to ambiguous segmentation. For any sta
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