一种扩散张量脑胼胝体图像分割算法  

Corpus Callosum Segmentation Algorithm of Diffusion Tensor Images

作　　者：王毅[1] 欧杨梅[1] 齐敏[1] 樊养余[1] 

机构地区：[1]西北工业大学电子信息学院,西安710072

出　　处：《计算机科学》2012年第12期257-260,271,共5页Computer Science

基　　金：国家自然科学基金(60903127;61202314);西北工业大学"翱翔之星计划"项目资助

摘　　要：提出了一种基于矢量活动轮廓模型的扩散张量脑胼胝体图像分割算法,其利用矢量Chan-Vese模型构造了控制轮廓线演化方向的矢量符号压力函数,并将向量范数形式用于表达脑胼胝体组织的扩散张量各向异性,给出了具有全局与局部分割特性的矢量活动轮廓模型。10组真实大脑扩散张量图像分割结果表明,该算法对脑胼胝体结构的分割精确、稳定。A vector-based active contour model algorithm for corpus callosum segmentation on diffusion tensor images was proposed.It utilized the vector-based Chan-Vese model to construct a vector-based signed pressure force function that controls the direction of the evolution.The form of the vector norm was used to describe anisotropy characteristics of corpus callosum on diffusion tensor images.The vector-based active contour model with both the global and the local segmentation property was also introduced into this algorithm.Segmentation results of 10 real diffusion tensor images showed that the proposed algorithm could segment corpus callosum precisely and stably.

关 键 词：扩散张量成像 脑胼胝体分割 灰度映射图 矢量活动轮廓模型 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]