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基于高斯-马尔科夫随机场模型的脑血管分割算法研究

ISSN号：1009-5896
期刊名称：电子与信息学报
时间：2014
页码：2053-2060
分类：TP319[自动化与计算机技术—计算机软件与理论;自动化与计算机技术—计算机科学与技术] TS201.4[轻工技术与工程—食品科学;轻工技术与工程—食品科学与工程]
作者机构：[1]Department of Information Science and Technology, Beijing Normal University, Beijing 100875, China, [2]Engineering Research Center of Virtual Reality and Applications, Ministry of Education, Beijing 100875, China, [3]Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
相关基金：The research is supported by the National Natural Science Foundation of China under Grant No. 61271366, and the National High Technology Research and Development 863 Program of China under Grant No. 2015AA020506.
相关项目：盘B样条和球B样条造型的理论及其应用

关键词：脑血管疾病, 并行化方法, 分割算法, 马尔可夫, 统计模型, 图像预处理, 有限混合模型, 参数估计, cerebrovascular segmentation, non-local means filtering, Markov random field, particle swarm optimization algorithm

中文摘要：

为从 time-of-flight 分割服的血容器，磁性的回声 angiography (TOF-MRA ) 精确地想象，我们与 Markov 随机的地(MRF ) 基于统计模型建议一个平行分割算法。第一，我们与基于补丁的 Fourier 转变改进传统的非局部的工具过滤器到 preprocess TOF-MRA 图象。在这步，我们主要利用 MRA 大脑图象顺序的稀疏和自我类似。第二，我们把 MRF 信息加到有限混合模式(FMM ) 适合医药图象的紧张分发。我们在图象顺序使用 MRF 估计服的纸巾的比例。最后，我们选粒子群优化(PSO ) 为算法到 parallelize FMM 的参数评价。很多实验特别为狭窄的容器验证我们的途径的高精确性和坚韧性。工作将在脑血管的疾病的预防和诊断上为医生提供重要帮助。

英文摘要：

For segmenting cerebral blood vessels from the time-of-flight magnetic resonance angiography （TOF-MRA） images accurately, we propose a parallel segmentation algorithm based on statistical model with Markov random field （MRF）. Firstly, we improve traditional non-local means filter with patch-based Fourier transformation to preprocess the TOF-MRA images. In this step, we mainly utilize the sparseness and self-similarity of the MRA brain images sequence. Secondly, we add the MRF information to the finite mixture mode （FMM） to fit the intensity distribution of medical images. We make use of the MRF in image sequence to estimate the proportion of cerebral tissues. Finally, we choose the particle swarm optimization （PSO） algorithm to parallelize the parameter estimation of FMM. A large number of experiments verify the high accuracy and robustness of our approach especially for narrow vessels. The work will offer significant assistance for physicians on the prevention and diagnosis of cerebrovascular diseases.

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