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

摘要： 许多水平集图像分割模型需要不断重新初始化水平集函数，或需要图像的梯度信息来约束曲线进化。提出最大化区域间差异性和距离约束函数水平集图像分割模型，该模型引入距离约束函数作为内部能量保证水平集函数始终为符号距离函数（SDF），避免了进化过程中对水平集函数的不断初始化。基于目标和背景两区域平均灰度值之差的平方构造外部能量函数（区域间差异性函数），并使其最大化，确保零水平集曲线稳定地收敛于目标边界。实验结果表明，提出的模型不仅有效地克服了传统模型需重新初始化的缺点，并且由于外部能量函数融合了区域信息，对弱边界图像以及含噪声图像具有较好分割能力。

关键词: 图像分割, 水平集, 重新初始化, 区域间差异性, 弱边界