A Parallel Markov Cerebrovascular Segmentation Algorithm Based on Statistical Model

• Published in: Journal of computer science and technology Vol. 31; no. 2; pp. 400 - 416
• Main Authors: Cao, Rong-Fei, Wang, Xing-Ce, Wu, Zhong-Ke, Zhou, Ming-Quan, Liu, Xin-Yu
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2016; Springer Nature B.V; Department of Information Science and Technology, Beijing Normal University, Beijing 100875, China%Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
• Subjects: Algorithms; Artificial Intelligence; Blood vessels; Cerebrovascular disease; Circulatory system; Collaboration; Computer Science; Data Structures and Information Theory; Eigenvalues; Image processing systems; Information Systems Applications (incl.Internet); Markov analysis; Markov models; Mathematical models; Medical imaging; Medical research; NMR; Noise; Nuclear magnetic resonance; Optimization; Optimization algorithms; Parameter estimation; Pattern recognition; R&D; Regular Paper; Research & development; Science; Segmentation; Set theory; Software Engineering; Statistical analysis; Studies; Swarm intelligence; Theory of Computation; 分割算法; 参数估计; 图像预处理; 并行化方法; 有限混合模型; 统计模型; 脑血管疾病; 马尔可夫; Markov random field; cerebrovascular segmentation; particle swarm optimization algorithm; non-local means filtering
• ISSN: 1000-9000; 1860-4749
• DOI: 10.1007/s11390-016-1634-6

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.