基于局部熵最小化的核磁共振脑图像二次分割算法  

Secondary Segmentation Algorithm for Magnetic Resonance Brain Image Based on Local Entropy Minimization

作　　者：车娜[1] 车翔玖[1] 高占恒[1] 王钲旋[1] 

机构地区：[1]吉林大学计算机科学与技术学院,长春130012

出　　处：《计算机研究与发展》2010年第7期1294-1303,共10页Journal of Computer Research and Development

基　　金：国家自然科学基金项目(60773098;60673021);博士学科点专项基金项目(20061083041)

摘　　要：医学图像分割在医学图像处理,尤其是在临床诊断的核磁共振图像分析中起着重要的作用.偏移场的存在使核磁共振脑图像中的局部统计特性发生变化,这成为自动化分割的一个主要障碍.为了克服偏移对分割造成的影响,提出了一种基于局部熵最小化的核磁共振脑图像二次分割算法.首先采取基于组织的分块算法和局部熵最小化以获得脑图像分割的聚类块,再以每个聚类块为中心进行动态搜索;利用模糊C均值算法对每个搜索窗口进行分割.将所有分割结果与原始聚类块的分割结果进行比较,对满足二次分割条件的像素进行二次分割.模拟数据和真实数据的实验结果表明,提出的二次分割方法准确、可靠.Medical image segmentation plays a very important role in medical image processmg, particularly in the clinical analysis of magnetic resonance （MR） brain images. Intensity inhomogeneity in MR images, which can change the local statistical characteristics of the images, becomes a maior obstacle to any automatic method for MR images. In order to reduce the influences of intensity inhomogeneity during segmentation, a secondary segmentation algorithm is presented for MR brain images based on local entropy minimization. By making use of the tissue-based method and local entropy minimization, the clustering blocks of brain image segmentation are gotton, and then the dynamic search is implemented by taking each clustering block as central region. For each dynamic searching-window, the fuzzy C-means algorithm is used to segment the images. Comparing all the segmentation results with them of originai clustering block, secondary segmentation is made for the pixels which hold the conditions of secondary segmentation. The segmentation results by using both simulated and real MR images show that the proposed secondary segmentation algorithm is accurate and reliable.

关 键 词：图像分割 MRI 偏移场 熵 二次分割 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]