基于模糊马尔可夫场的脑部MR图像分割算法  

Novel algorithm for segmentation of brain MR images using fuzzy Markov random field

作　　者：李彬[1] 陈武凡[1] 颜刚[1] 

机构地区：[1]南方医科大学生物医学工程学院,广州510515

出　　处：《计算机工程与应用》2007年第7期14-16,19,共4页Computer Engineering and Applications

基　　金：国家重点基础研究发展规划(973)(the National Grand Fundamental Research 973 Program of China under Grant No.2003CB716101)

摘　　要：在传统马尔可夫场模型的基础上,建立了模糊马尔可夫场模型。通过对模型的分析得出图像像素对不同类的隶属度计算公式,提出了一种高效、无监督的图像分割算法,从而实现了对脑部MR图像的精确分割。通过对模拟脑部MR图像和临床脑部MR图像分割实验,表明新算法比传统的基于马尔可夫场的图像分割算法和模糊C-均值等图像分割算法有更精确的图像分割能力。In this paper,a fuzzy Markov Random Field（MRF） model is developed based on the conventional MRF model.By analyzing the fuzzy MRF model,the formula of determining the membership values for each voxel＇to indicate the partial volume degree is derived.We also propose an efficient and unsupervised algorithm to realize the accurate segmentation for MR brain images.The simulated brain images and real clinical images are selected to test the proposed algorithm.The experimental results show that the proposed algorithm can segment the brain images more accurately than the conventional model-based algorithms and the fuzzy C-mean do as well.

关 键 词：磁共振图像 图像分割 模糊马尔可夫场 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]