东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2012, Vol. 33 ›› Issue (5): 645-648.DOI: -

• 论著 • 上一篇    下一篇

一种新的脑白质分割方法

孟琭;苗长胜;王丽娟;   

  1. 东北大学信息科学与工程学院;辽宁大学轻型产业学院;
  • 收稿日期:2013-06-19 修回日期:2013-06-19 发布日期:2013-04-04
  • 通讯作者: -
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(61101057,61001047)

A new method of brain white matter segmentation

Meng, Lu (1); Miao, Chang-Sheng (1); Wang, Li-Juan (2)   

  1. (1) School of Information Science and Engineering, Northeastern University, Shenyang 110819, China; (2) Light Industry College, Liaoning University, Shenyang 110036, China
  • Received:2013-06-19 Revised:2013-06-19 Published:2013-04-04
  • Contact: Meng, L.
  • About author:-
  • Supported by:
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摘要： 基于扩散张量成像提出一种新的脑白质分割方法.首先,计算扩散张量成像的各向异性参数和扩散参数,并得到各个参数下的脑部图像;然后,通过期望值最大化(expectation maximization,EM)模型求得各个各向异性参数图像的脑白质和非脑白质区域;最后,通过STAPLE(simultaneous truth and performancel evel estimation)模型融合各个DTI参数图像分割结果,得到脑白质分割结果.实验结果表明,该方法具有较好的分割效果,能有效地从脑组织中分割出脑白质.

关键词: 扩散张量成像, 脑组织分割, EM模型, STAPLE模型

Abstract: A novel brain structure segmentation algorithm based on diffusion tensor imaging (DTI), which can extract white matter (WM) is presented. First, DTI diffusion and anisotropy parameters are computed to achieve brain images of each DTI parameter channel. Then, using the expectation maximization (EM) model, WM/non-WM regions in the anisotropy parameters channel are calculated. Finally, using simultaneous truth and performance level estimation (STAPLE) model, segmentation results of each DTI parameter image are fused to achieve the final brain white matter structure segmentation results. Experiments' results showed that the algorithm can accurately segment, WM in brain structures.

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