Abstract−Image segmentation has a long tradition as one of the fundamental problems in computer vision. Level Sets are an important category of modern image segmentation techniques are based on partial differential equations (PDE), i.e. progressive evaluation of the differences among neighboring pixels to find object boundaries. Earlier method used novel level set method (LSM) for image segmentation. This method used edges and region information for segmentation of objects with weak boundaries. This method designed a nonlinear adaptive velocity and a probability-weighted stopping force by using Bayesian rule. However the difficulty of image segmentation methods based on the popular level set framework to handle an arbitrary number of regions. To address this problem the present work proposes Multi Region Level Set Segmentation which handles an arbitrary number of regions. This can be explored with addition of shape prior's considerations. In addition apriori information of these can be incorporated by using Bayesian scheme. While segmenting both known and unknown objects, it allows the evolution of enormous invariant shape priors. The image structures are considered as separate regions, when they are unknown. Then region splitting is used to obtain the number of regions and the initialization of the required level set functions. In the next step, the energy requirement of level set functions is robustly minimized and similar regions are merged in a last step. Experimental result achieves better result when compare with existing system
