基于高斯-马尔科夫随机场模型的脑血管分割算法研究

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。

A Novel Cerebrovascular Segmentation Algorithm Based on Gauss-Markov Random Field Model

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.