Active contour model based on local and global information for image segmentation

According to Bayesian classification criteria, an improved level set method for image segmentation based on local and global information is proposed. Firstly, a local energy term based on local intensity information is defined. It can guide the evolving curve near the target settled on the boundaries. Secondly, a global energy term is built according to the global intensity information, so as to accelerate the evolution of the evolving curve far away from the target. Finally, a unified level set framework is proposed which combines the local energy term and global energy term together to improve the efficiency of segmentation and deal with images with intensity inhomogeneity. Experimental results show that this model is robust to the position of initial contour. In addition, it can obtain prod satisfying results in segmenting images with intensity inhomogeneity.