摘要
多发性硬化症是一种严重威胁中枢神经功能的疾病,对其病灶自动检测方法的研究正受到越来越多的关注。基于D-S证据理论和模糊C-均值(FCM)聚类算法,提出了一种融合T1和T2加权MR图像信息的多发性硬化症自动分割算法。首先运用FCM聚类算法分别分割T1和T2加权MR图像,然后利用根据D-S证据理论得到的融合两种加权图像信息的基本概率分配函数,实现多发性硬化症病灶的分割。通过对多发性硬化症MR脑部图像的分割实验表明,该算法具有很高的多发性硬化症病灶分割精度,对多发性硬化症的临床辅助诊断具有重要作用。
Multiple sclerosis （MS） is an inflammatory demyelinating disease that would damage central nervous system. There was a growing attention to the segmentation algorithms of MS lesions. This paper developed an automatic algorithm for MS le- sions segmentation by utilizing the fusion T1 and T2-weighted MR brain images based on D-S evidence theory and FCM clustering algorithm. First, segmented T1 and T2 -weighted MR brain images by a FCM clustering algorithm. Then fused the resultant images according to the joint mass of T1 and T2-weighted MR brain images to produce the segmentation of MS lesions. The tes- ting experiments on MR brain images show that the proposed algorithm is able to improve the segmentation accuracy, which is important to assist the diagnosis of MS in clinic.
出处
《计算机应用研究》
CSCD
北大核心
2011年第1期378-380,共3页
Application Research of Computers
基金
国家"973"重点基础研究发展规划资助项目(2010CB732500)
关键词
图像分割
D—S证据理论
模糊C-均值聚类
信息融合
多发性硬化症
image Segmentation
D-S evidence theory
fuzzy C-mean clustering
information fusion
multiple sclerosis lesions
