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Volume 31 Issue 2
March  2016
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Rong-Fei Cao, Xing-Ce Wang, Zhong-Ke Wu, Ming-Quan Zhou, Xin-Yu Liu. A Parallel Markov Cerebrovascular Segmentation Algorithm Based on Statistical Model[J]. Journal of Computer Science and Technology, 2016, 31(2): 400-416. DOI: 10.1007/s11390-016-1634-6
Citation: Rong-Fei Cao, Xing-Ce Wang, Zhong-Ke Wu, Ming-Quan Zhou, Xin-Yu Liu. A Parallel Markov Cerebrovascular Segmentation Algorithm Based on Statistical Model[J]. Journal of Computer Science and Technology, 2016, 31(2): 400-416. DOI: 10.1007/s11390-016-1634-6
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A Parallel Markov Cerebrovascular Segmentation Algorithm Based on Statistical Model

  • 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

Funds: 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.
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  • Author Bio:

    Rong-Fei Cao received her B.S. degree in computer science from North West University, Xi'an, in 2011. Now she is a master student in Beijing Normal University, Beijing. Her current research interests include medical image processing and 3D analysis.

  • Corresponding author:

    Xing-Ce Wang E-mail: wangxingce@bnu.edu.cn

  • Received Date: April 19, 2014
  • Revised Date: July 20, 2015
  • Published Date: March 04, 2016
    Abstract
  • 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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    • References (39)
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