基于 Markov 随机场的脑部三维磁共振血管造影数据的分割

文章提出了脑部核磁共振血管造影(Magnetic Resonance Angiography, MRA)的全自动分割方法，该方法 有效增强了现有的基于 Markov 随机场(Markov Random Field, MRF)的分割技术。现有的三维 Markov 分割模型通 常面临的挑战是：(1)低级 MRF 模型参数初始化不够准确；(2)普通的 MRF 邻域系统无法探测精细的血管结构。 针对这两类问题，分别提出了基于多尺度滤波响应阈值分析和多模式邻域系统进行解决，使得 MRF 模型的血管 分辨率提高到 2 个体素的细小血管。实验中，低级模型参数的精确估计采用了最大期望算法，高阶 MRF 参数的 估计采用最大伪似然估计方法；通过三维仿真数据和实际脑部 MRA 数据进行验证，分割结果显示了较小的全局 误差

Segmentation of Three-Dimensional Data of Brain Magnetic Resonance Angiography Based on Markov Random Field

In this paper, a full automatic method was proposed for the segmentation of brain magnetic resonance angiography (MRA) dataset, which improved the technologies of Markov random field (MRF). Existing 3D-MRF models generally faced some challenges including: (1) The parameter initialization of low level MRF model is not accurate; (2) The ordinary neighborhood system cannot deal with local fine vessel structure. Aiming to solve the two problems, the multiscale filtering with threshold analysis and a multi-pattern neighborhood system were proposed, respectively. Such method enabled the MRF model delineating vessels to be as small as two voxels in diameters. In the experiments, the parameters of the low level MRF model were estimated using the expectation maximization algorithm, while the parameters of the high level MRF models were estimated based on the maximum pseudo likelihood algorithm. A set of phantoms and some MRA clinical datasets were used to validate the algorithms, to yield smaller segmentation errors.