无线电通信技术2016，Vol.42Issue(4)：33-37,5.DOI:10.3969/j.issn.1003-3114.2016.04.09
基于模糊核聚类的双水平集医学图像分割
Double Level Set Segmentation of Medical Images Based on Fuzzy Kernel Clustering
摘要
Abstract
In the practical medical images,there are two or more target areas except the target and background region,the tradition⁃al Chan⁃Vese model is only applied to two phase image segmentation generally,not well to multiphase image segmentation.This paper proposes a modified double level set segmentation of medical images based on fuzzy kernel clustering,which reduces image noise and sensitivity of the double level set model by using KFCM clustering algorithm. The double level set model is improved for secondary segmentation after clustering.This method has good capability to suppress image noise,and can make full use of image edge information without initializing level set function,reduce the number of iterations and computation,and effectively achieve heterogeneous region seg⁃mentation.关键词
图像分割/KFCM/C-V模型/双水平集Key words
image segmentation/KFCM/C-V model/double level set分类
信息技术与安全科学引用本文复制引用
张辉,朱家明,吴杰..基于模糊核聚类的双水平集医学图像分割[J].无线电通信技术,2016,42(4):33-37,5.基金项目
国家自然科学基金项目 （）
