虽然Snake模型是一种有效的基于参数的轮廓探测方法，但由于其对初始位置过于敏感，不但参数选取缺乏严格的理论指导，且不能处理拓扑结构改变的问题。为此，针对Snake模型在弱边缘处容易溢出等不足，首先通过引入区域信息对Snake模型的图像力进行了修正，然后对Snake模型容易陷入局部极小化的问题，利用粒子群优化算法的全局优化特性和良好的数值稳定性来对Snake模型的分割结果进行优化。人工合成图像和医学图像的实验结果表明，该方法是有效的。

Snake model is a kind of deformable image segmentation model based on parameters and has been proved effective to contour detecting as well. It is sensitive to the position of the initial curve, lacks the theoretical guidance to choose parameters and can not deal with the change of topological structure. Snake model is easy to leak out if the edge is weak. This paper presents a modified image force by integrating the region information to improve it. After that, Particle Swarm Optimization (PSO) algorithm is applied to optimize the segmentation results obtained by Snake model. The encouraging results have been shown by experiments with the synthesis images and medical images.