| 基于高斯-马尔科夫随机场模型的脑血管分割算法研究 |
| |
| 引用本文： | 曹容菲, 张美霞, 王醒策, 武仲科, 周明全, 田沄, 刘新宇. 基于高斯-马尔科夫随机场模型的脑血管分割算法研究[J]. 电子与信息学报, 2014, 36(9): 2053-2060. doi: 10.3724/SP.J.1146.2013.01534 |
| |
| 作者姓名： | 曹容菲 张美霞 王醒策 武仲科 周明全 田沄 刘新宇 |
| |
| 作者单位： | 1. 北京师范大学信息科学与技术学院 北京 100875
2. 中国科学院计算技术研究所 北京100190 |
| |
| 基金项目： | 国家自然科学基金，中央高校基本科研业务费专项基金，首都科技条件平台项目(Z131110000613062)资助课题 |
| |
| 摘 要： | 由于脑血管具有分枝众多、形态细小以及位置特殊和形态复杂等特性，在医学图像中精确地提取脑血管成为一项比较棘手的问题。该文提出了一种新颖的统计学分割方法，有效地实现了脑血管的精确分割。首先，充分利用各血管像素的空间邻域信息，将马尔科夫随机场信息加入到统计学模型的方法中，提出了新的马尔科夫统计模型；然后，利用随机期望最大化(Stochastic versions of the Expectation Maximization, SEM)算法来对统计模型中的多个参数进行估计，寻找最优解，进而实现了脑血管的3维分割。实验结果表明，该方法不仅能够分割出较大的血管分支，而且因其考虑了血管邻域信息，对细小血管的分割也有较好的效果，因此对脑血管疾病的临床预防和诊断具有深远的意义。
|
| 关 键 词： | 脑血管分割 马尔科夫随机场 统计模型 随机期望最大化算法 |
| 收稿时间： | 2013-10-10 |
| 修稿时间： | 2014-03-21 |
A Novel Cerebrovascular Segmentation Algorithm Based on Gauss-Markov Random Field Model |
| |
| Cao Rong-Fei, Zhang Mei-Xia, Wang Xing-Ce, Wu Zhong-Ke, Zhou Ming-Quan, Tian Yun, Liu Xin-Yu. A Novel Cerebrovascular Segmentation Algorithm Based on Gauss-Markov Random Field Model[J]. Journal of Electronics & Information Technology, 2014, 36(9): 2053-2060. doi: 10.3724/SP.J.1146.2013.01534 |
| |
| Authors: | Cao Rong-fei Zhang Mei-xia Wang Xing-ce Wu Zhong-ke Zhou Ming-quan Tian Yun Liu Xin-yu |
| |
| Abstract: | In order to solve the thorny cerebrovascular segmentation problems about cerebral vessels of many branches, small shape, special position and complex patterns, this paper presents a novel statistical method to achieve effectively the accurate segmentation of cerebral vessels. Firstly, the Markov random field information is added to the statistical model which makes the full use of the spatial neighborhood information of each pixel and a new Markov statistical model is proposed; then Stochastic versions of the Expectation Maximization (SEM) algorithm is used to estimate parameters of the Markov model and the optimal solution is found, which finishes the three-dimensional cerebrovascular segmentation. Experimental results show that the proposed method can not only segment the large vessel branches, but also have a good effect on small vessels segmentation because of considering neighborhood information of each pixel. Therefore, the proposed method also has the far-reaching significance to the clinical prevention and diagnosis of cerebrovascular diseases. |
| |
| Keywords: | Cerebrovascular segmentation Markov Random Field (MRF) Statistical model Stochastic versions of the Expectation Maximization (SEM) algorithm |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
| 点击此处可从《电子与信息学报》浏览原始摘要信息 |
|
点击此处可从《电子与信息学报》下载全文 |
