关键词: 图像分割, 水平集, 重新初始化, 区域间差异性, 弱边界

Abstract: In most existing level set models for image segmentation，it is necessary to constantly re-initialize the level set function，or to acquire the gradient flow information of the image to restrict the evolution of the curve.A novel image segmentation model of level set based on the maximization of the inter-region dissimilarity and the distance-based constraint function is proposed.In this model，the distance-based constraint function is introduced as the internal energy to ensure that the level set function is always the Signal Distance Function（SDF），so that the constant re-initialization of the level set function during the evolution process is avoided.Meanwhile，the external energy function（inter-region dissimilarity function） is constructed based on the square of the difference between the average grey levels of the target area and the background.This function is maximized to ensure that the zero level set curve converges to the target boundary stably.Experimental results show that the constant re-initialization in traditional models has been eliminated in the proposed model.Furthermore，since region information has been incorporated into the energy function，the model renders good performance in the segmentation of both images with weak edges and those with noise.

Key words: image segmentation, level set, re-initialization, inter-region dissimilarity, weak edge