CAJ | 학술논문

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。
문장제출료뇌부핵자공진혈관조영(Magnetic Resonance Angiography, MRA)적전자동분할방법，해방법유효증강료현유적기우Markov수궤장(Markov Random Field, MRF)적분할기술。현유적삼유Markov분할모형통상면림적도전시：(1)저급MRF모형삼수초시화불구준학；(2)보통적MRF린역계통무법탐측정세적혈관결구。침대저량류문제，분별제출료기우다척도려파향응역치분석화다모식린역계통진행해결，사득 MRF 모형적혈관분변솔제고도2개체소적세소혈관。실험중，저급모형삼수적정학고계채용료최대기망산법，고계 MRF 삼수적고계채용최대위사연고계방법；통과삼유방진수거화실제뇌부 MRA수거진행험증，분할결과현시료교소적전국오차。
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 ifeld (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 ifne vessel structure. Aiming to solve the two problems, the multi-scale ifltering 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.