基于非齐次Markov随机场的无监督图像分割新算法  

A new unsupervised algorithm for image segmentation based on an inhomogeneous Markov random field model

作　　者：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》2007年第11期1646-1648,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)~~

摘　　要：提出了一种基于非齐次Markov随机场(MRF)的图像分割算法,并运用模糊空间元素的模糊相似度模型,估计非齐次MRF的参数,改进了传统的基于齐次MRF的图像分割算法。通过对模拟脑部MR图像和临床脑部MR图像分割实验,表明该算法比传统的基于齐次MRF的图像分割算法和模糊C-均值等图像分割算法具有更精确的图像分割能力,并且运算简单、运算速度快、稳健性好。A new unsupervised algorithm for image segmentation is proposed using an inhomogeneous Markov random field （MRF） model, in which the parameter is estimated in fuzzy spel affinities. The proposed algorithm improved the accuracy of segmentation. Simulated brain MR image with different noise levels and clinical brain MR image were presented in the experiments. The results showed that the proposed algorithm was more powerful than conventional homogeneous MRF model-based ones and than the fuzzy c-means clustering algorithm as well.

关 键 词：图像分割 MARKOV随机场 模糊相似度 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]