中文摘要：

针对分水岭图像分割算法对噪声敏感和易于产生过分割现象，提出了一种基于粒子群和区域生长的改进分水岭算法。该算法将区域生长与分水岭分割算法相结合，依据香农熵构建一个目标函数，确定区域生长参数；利用灰度均值计算区域间的差异度，将比较小的区域合并到与之相邻的差异度最小的区域中；利用粒子群算法对该目标函数进行全局寻优，实现图像分割。实验证明新算法较已有的几种分割算法有了很大提高，并有效地解决了分水岭算法的过分割问题，分割结果更加符合人的直观视觉特性，是一种有效、准确且实用的图像分割方法。

英文摘要：

An improved watershed image segmentation algorithm based on particle swarm and region growing was proposed to solve the problems of noisesensitivity and over-segmentation. The improved algorithm, combining region growing with the classical watershed algorithm, was established by constructing an objective function based on Shannon entropy to determine the parameter of the region growing. The regional disparity degree was calculated by the gray mean, and the smaller region was merged with the neighbor region with a minimal disparity degree. The particle swarm optimization algorithm was employed to search the global optimization of the objective function. Ex-perimental results show that this improved algorithm is better than other image segmentation methods, and can solve effectively the problem of over-segmentation that existed with the watershed algorithm. The segmentation results con-form to the visual characteristics of the human eye, so this algorithm is therefore an effective, accurate, and practi-cal image segmentation method.