基于均值距离的图像分割方法

针对医学图像分割中存在的分割类数不易确定的问题,利用常用均值间的不等式关系构造出了一种新的分割类数判据--均值距离函数,并将均值距离函数与模拟退火算法相结合,提出了一种基于均值距离的分割算法。该算法以均值距离函数作为目标函数,采用模拟退火算法进行优化,在整个搜索空间中寻找最优分割阈值,弥补了模糊C均值算法(fuzzy C-means,FCM)分类类数难以确定、搜索过程容易陷入局部极值的缺陷。实验结果表明,算法对含有病灶的医学图像能够进行自动分割,并且分割速度明显高于基于互信息的分割方法。

An image segmentation method based on mean divergence

In the research of medical image segmentation, it is difficult to determine the number of segmentation classes. To solve the problem, a novel measurement for determining the number of classes named mean divergence function was formed according to the relation among three common means. And then an image segmentation method based on mean divergence and simulated annealing was proposed. In this method, the mean divergence function is used as an optimization object and simulated annealing is used as an optimization method to find the optimal segmentation threshold in overall search space. This overcomes the shortcomings of fuzzy C-means (FCM) clustering algorithm, such as it is hard to determine the number of classes and easy to get into a local extremum. Experimental results show that this method could automatically segment the medical image with focus, and the speed had significant improvement compared with the method based on mutual information.