改进的模糊C均值聚类算法  

Improved Method for Fuzzy C-Means Clustering Algorithm

作　　者：林亚忠[1,2] 张会奇[3] 李新[3] 栾钦波[3] 

机构地区：[1]解放军第175医院 [2]厦门大学附属东南医院,福建漳州363000 [3]厦门大学计算机科学系,福建厦门361005

出　　处：《临床医学工程》2013年第4期385-388,共4页Clinical Medicine & Engineering

基　　金：南京军区重点项目(项目编号:11Z023);福建省自然科学基金项目(项目编号:2008J0312)

摘　　要：由于传统的模糊C均值(fuzzy C-means,FCM)算法没有考虑像素点的空间邻域信息,仅涉及像素的单点灰度,在处理含有噪声的图像时有很大的局限性,因此分割效果较差。针对FCM的缺陷,提出一种新的改进算法,该算法引入Gibbs随机场,将Gibbs随机场先验概率与像素点隶属度的乘积作为新的像素隶属度。实验表明,改进后的算法有良好的分割效果,既可以较为完整地保留图像边界细节,又能较好地去除图像的噪声。The traditional fuzzy C-means （FCM） algorithm has great limitations in dealing with the noisy images owing to not considering the spatial information of the pixels and only involving the pixel gray of a single point, so it＇s poor in segmenting an image. For the defects of the FCM algorithm, a new improved algorithm is proposed in this article, in which a product of Gibbs priori probability and the membership is regarded as the new pixel membership. Experimental results show that the improved algorithm has a good segmentation result; it can retain more complete edge details of image and can remove the image noise more effectively.

关 键 词：模糊C均值 聚类 噪声 GIBBS随机场 

分 类 号：R318.6[医药卫生—生物医学工程] TP391[医药卫生—基础医学]