基于模糊核聚类的MR图像分割新算法  

A new algorithm for magnetic resonance image segmentation based on fuzzy kerne1 clustering

作　　者：余学飞[1] 李彬[1] 陈武凡[1] 

机构地区：[1]南方医科大学生物医学工程学院,广东广州510515

出　　处：《南方医科大学学报》2008年第4期555-557,共3页Journal of Southern Medical University

基　　金：国家973重点基础研究发展规划项目(2003CB716101);广东省科技计划项目(2007B010400057)

摘　　要：在传统的模糊聚类算法中引入了核函数,同时引入了控制邻域作用的约束项,提出了改进的基于模糊核聚类的MR图像分割新算法。通过对模拟图和仿真的脑部MR图像的分割实验,证明本算法可以有效地分割含有噪声的图像。Fuzzy clustering technique is a popular model widely used in the segmentation of magnetic resonance （MR） images. However, when the conventional fuzzy clustering algorithm is used for image segmentation, the algorithm strictly depending on the current pixels works only on images with less noise. In the paper, we presented a modified fuzzy kernel clustering algorithm for MR image segmentation. The new algorithm incorporates a kernel-induced distance mertric and a penalty term that controls the neighborhood effect to the objective function. The results of experiment on both the synthetic images and simulated MR images show that the proposed algorithm is more robust to noise than the standard fuzzy image segmentation algorithms.

关 键 词：图像分割 模糊核聚类 邻域信息 磁共振图像 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]