1 October 2011 Statistical approaches to automatic level set image segmentation with multiple regions
Author Affiliations +
Optical Engineering, 50(10), 107001 (2011). doi:10.1117/1.3631042
This study is to investigate a new representation of a partition of an image domain into a number of regions using a 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 three terms: a Bayesian term based on the prior probability, a regularity term adopted to avoid the generation of excessively irregular and small segmented regions, and a term based on a region merging prior related to region area, which is applied to allow the number of regions to vary automatically during curve evolution and therefore can optimize the objective functional implicitly with respect to the number of 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 times during curve evolution. Given these advantages, the proposed method can get good performance and experiments show promising segmentation results on both synthetic and real images.
Jiangxiong Fang, Jie Yang, Enmei Tu, Zhenghong Jia, Nikola K. Kasabov, Cuiyin Liu, "Statistical approaches to automatic level set image segmentation with multiple regions," Optical Engineering 50(10), 107001 (1 October 2011). https://doi.org/10.1117/1.3631042


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