基于子区域相似度的医学图像分割算法

将传统的区域生长算法思想融入到一种轮廓线逼近方法中。通过定义子区域的相似度准则,利用围绕像素的子区域的统计相似性，作为一个初始多边形轮廓演化的驱动因子，从粗到细，实现了对目标区域的逼近分割。实验表明，所提算法具有较好的抗噪性和较高的分割效率，可以有效分割出医学图像中的目标区域。

New medical image segmentation algorithm based on subregion similarity

Introducing the traditional region growing method into a contour approximating method, a new segmentation algorithm based on subregion similarity was presented. Firstly, an initial polygon contour was brought in by human-computer interactive process. Then the region of interest was obtained by using a sub-region similarity which served as driving force of the contour evolution. The experimental results show that the algorithm is of more anti-noise ability than the region growing method, and can divide up the region of interest efficiently.