基于全局LBF水平集模型的脑血管层次粗分割

王醒策1; 张美霞1; 武仲科1; 周明全1; 曹容菲1; 田沄1; 刘新宇2

doi:10.3788/OPE.20132112.3283

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基于全局LBF水平集模型的脑血管层次粗分割

信息科学 | 更新时间：2020-08-12

- 基于全局LBF水平集模型的脑血管层次粗分割
- Level Coarse Brain Vessel Segmentation Based on Global LBF Model
- 光学精密工程 2013年21卷第12期页码：3283-3297
- 作者机构：
  
  1. Tokyo,Japan,182-8585
  2. 北京师范大学信息科学与技术学院, 北京&nbsp
  3. 中国科学院计算技术研究所前瞻研究实验室北京,100190
- 作者简介：
- 基金信息：
  
  基于球B样条的Willis环建模、分割及定位关键技术研究();脑血管兴趣区域提取关键技术研究();基于统计分割的脑血管三维模型重构研究();盘B样条和球B样条造型的理论
- DOI：10.3788/OPE.20132112.3283
  中图分类号： TP391
- 收稿日期：2013-03-19，
  
  修回日期：2013-05-27，
  
  网络出版日期：2013-12-25，
  
  纸质出版日期：2013-12-25
- 稿件说明：
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王醒策, 张美霞, 武仲科, 周明全, 曹容菲, 田沄, 刘新宇. 基于全局LBF水平集模型的脑血管层次粗分割[J]. 光学精密工程, 2013,21(12): 3283-3297

WANG Xing-Ce, ZHANG Mei-Xia, WU Zhong-Ke, ZHOU Ming-Quan, CAO Rong-Fei, TIAN Yun, LIU Xin-Yu. Level Coarse Brain Vessel Segmentation Based on Global LBF Model[J]. Editorial Office of Optics and Precision Engineering, 2013,21(12): 3283-3297
王醒策, 张美霞, 武仲科, 周明全, 曹容菲, 田沄, 刘新宇. 基于全局LBF水平集模型的脑血管层次粗分割[J]. 光学精密工程, 2013,21(12): 3283-3297 DOI： 10.3788/OPE.20132112.3283.

WANG Xing-Ce, ZHANG Mei-Xia, WU Zhong-Ke, ZHOU Ming-Quan, CAO Rong-Fei, TIAN Yun, LIU Xin-Yu. Level Coarse Brain Vessel Segmentation Based on Global LBF Model[J]. Editorial Office of Optics and Precision Engineering, 2013,21(12): 3283-3297 DOI： 10.3788/OPE.20132112.3283.

摘要

考虑对脑血管进行三维分割具有一定难度，提出了一种基于全局LBF (Local Binary Fitting)水平集模型的脑血管层次化粗分割方法。首先，应用定向加权中值（DWM）滤波和各向异性扩散滤波去除脑图像噪声，同时保存血管边缘信息，在多尺度条件下局部梯度最大（LIGM)算法，应用灰度和梯度信息提取备选血管，基本实现脑灰质去除。然后，改进全局信息LBF水平集算法实现最大强度投影（MIP）图像分割，采用形态信息提取备选血管，剔除干扰组织。最后，融合两种方法实现脑血管粗提取。实验表明，层次化的分割方法可去除大部分不相关脑组织，包含直接双高斯统计模型中的所有分割血管信息。本项研究基于时飞磁共振血管造影（TOF_MRA)数据，相关研究结果可扩展到其它相似系统中。

Abstract

To solve the problem that human brain vessels are difficult to be segmented

a level coarse brain vessel segmentation based on the global Local Binary Fitting(LBF) model was presented in the paper. First

the Directional Weight Median(DWM） filtering and the anisotropic diffusion model were used to reduce the noise and to enhance the vessel edges of brain images. Then

the Local Intensity Gradient Maximum(LIGM) algorithm was implemented based on a multi-scale space. The information of intensity and gradient was used to get the vessel candidate set and remove the influence of gray matter in the brain. At the same time

the improved global LBF level set model was used to segment the Maximum Intensity Projection(MIP) image. The vessel voxels were extracted with the conformation information. The results of these two steps were fused together to get the minimal covering set of the brain vessel. The experimental results show that all most the vessel voxels directly segmented by the double Gauss model can be reserved and most uncorrelated voxels can be removed. This research is based on the Time of Flight Magnetic Resonance Angiography(TOF MRA) and it is easy to expand to the similar system.

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references

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