东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2011, Vol. 32 ›› Issue (4): 489-492.DOI: -

• 论著 • 上一篇    下一篇

基于改进C-V模型的脑组织提取算法

贾迪;杨金柱;张一飞;于戈;   

  1. 东北大学医学影像计算教育部重点实验室;东北大学信息科学与工程学院;
  • 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(61001047)

A brain tissue extraction algorithm based on an improved C-V model

Jia, Di (1); Yang, Jin-Zhu (1); Zhang, Yi-Fei (2); Yu, Ge (2)   

  1. (1) Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang 110179, China; (2) School of Information Science and Engineering, Northeastern University, Shenyang 110819, China
  • Received:2013-06-19 Revised:2013-06-19 Published:2013-04-04
  • Contact: Jia, D.
  • About author:-
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摘要： 通过对水平集算法C-V模型进行三方面改进,提出一种基于改进C-V模型的脑组织提取算法.首先,通过改进经典的距离函数,在保证准确度的同时,可以在很大程度上加快距离函数的收敛,提高分割速度.其次,改进了收敛结果的唯一性,使边缘停止在欲提取的目标类灰度上,用于去除脑脊液,使脑灰质、白质的提取更加准确.最后,提出了迭代收敛的动态结束条件.通过间隔帧的对比,改进了以往通过经验迭代次数作为结束条件,使分割效果与迭代时间达到较优.实验结果表明,该算法在脑组织的分割速度及准确性上有了较大提高,为医生确诊病情带来便利.

关键词: 脑组织分割, 水平集, C-V模型, 区域增长, 活动轮廓模型

Abstract: Through improving C-V model in three aspects, a new rapid and accurate method for brain tissue extraction is presented. First, segmentation speed was enhanced through improving the classical distance matrix, with the distance function convergence improved compromising accuracy. Secondly, the improved method changed the uniqueness of classical results and made the evolving lines stop at the same level gray, so spinal fluid could be wiped off, and white/gray matter was extracted more accurately. Finally, a dynamic condition for ending iteration was proposed through comparing the interval frames. This improvement changed the flaw of setting evolving times to end iteration, and improved veracity and speed. Results indicated that these improvements make brain tissue extraction more rapid and accurate, and could be helpful to doctors in making confirmed diagnoses.

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