Color Image Segmentation Based on Adaptive Level Set and Region Statistical Information

This paper proposed an adaptive level set algorithm combining region statistical information for color image segmentation. In this model, we deduce image segmentation model with Maximum a posteriori according to Bayesian theory, and combine it into GAC model. Firstly, construct a speed stop function based on region statistical information which combines region statistical character and Bayesian model. And apply it to image segmentation on color images. Secondly, construct alterable coefficient based on a posteriori of segmented image of inside and outside the curve. It aims to change evolution direction adaptively based on the information of image. Thirdly, eliminate the re-initialization procedure by introducing interior restrict energy term of the Li method. At the same time, simplify the definition of the initial level set function. Experimental results show that this method has the advantages of evolving adaptively and stable numerical solution, and can extract the real edge of object from color image efficiently, so that is an effective image segmentation method.