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A Parallel Markov Cerebrovascular Segmentation Algorithm Based on Statistical Model

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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

  1. Park M, Kang B, Jin S J et al. Computer aided diagnosis system of medical images using incremental learning method. Expert Systems with Applications, 2009, 36(3): 7242–7251.

    Article  Google Scholar 

  2. Kirbas C, Quek F. A review of vessel extraction techniques and algorithms. ACM Computing Surveys (CSUR), 2004, 36(2): 81–121.

    Article  Google Scholar 

  3. Lesage D, Angelini E D, Bloch I, Funka-Lea G. A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes. Medical Image Analysis, 2009, 13(6): 819–845.

    Article  Google Scholar 

  4. Kass M, Witkin A, Terzopoulos D. Snakes: Active contour models. International Journal of Computer Vision, 1988, 1(4): 321–331.

    Article  MATH  Google Scholar 

  5. Li C, Xu C, Gui C et al. Level set evolution without re-initialization: A new variational formulation. In Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2005, pp.430-436.

  6. Li C, Kao C Y, Gore J C et al. Minimization of regionscalable fitting energy for image segmentation. IEEE Transactions on Image Processing, 2008, 17(10): 1940–1949.

    Article  MathSciNet  Google Scholar 

  7. Wilson D L, Noble J A. An adaptive segmentation algorithm for time-of-flight MRA data. IEEE Transactions on Medical Imaging, 1999, 18(10): 938–945.

    Article  Google Scholar 

  8. Hassouna M S, Farag A A, Hushek S et al. Cerebrovascular segmentation from TOF using stochastic models. Medical Image Analysis, 2006, 10(1): 2–18.

    Article  Google Scholar 

  9. Fang K, Wang D F, Lui L M et al. 3D model-based method for vessel segmentation in TOF-MRA. In Proc. International Conference on Machine Learning and Cybernetics (ICMLC), Jul. 2011, pp.1607-1611.

  10. Yi J, Ra J B. A locally adaptive region growing algorithm for vascular segmentation. International Journal of Imaging Systems and Technology, 2003, 13(4): 208–214.

    Article  Google Scholar 

  11. Eiho S, Sekiguchi H, Sugimoto N et al. Branch-based region growing method for blood vessel segmentation. In Proc. International Society for Photogrammetry and Remote Sensing Congress, July 2004, pp.796-801.

  12. Sato Y, Nakajima S, Atsumi H et al. 3D multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. In Proc. the 1st CVRMed-MRCAS, March 1997, pp.213-222.

  13. Frangi A F, Niessen W J, Vincken K L et al. Multiscale vessel enhancement filtering. In Proc. the 1st MICCAI, October 1998, pp.130-137.

  14. Wörz S, Rohr K. Segmentation and quantification of human vessels using a 3-D cylindrical intensity model. IEEE Transactions on Image Processing, 2007, 16(8): 1994–2004.

  15. Zhang J, Zheng J, Cai J. A diffusion approach to seeded image segmentation. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2010, pp.2125-2132.

  16. Xin S Q, He Y, Fu C W et al. Euclidean geodesic loops on high-genus surfaces applied to the morphometry of vestibular systems. In Proc. Medical Image Computing and Computer-Assisted Intervention, September 2011, pp.384-392.

  17. Feng X, Wang X, Zhou M et al. Segmentation algorithm of brain vessel image based on SEM statistical mixture model. In Proc. the 7th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), August 2010, pp.1830-1833.

  18. Tian Y, Duan F, Lu K et al. A flexible 3D cerebrovascular extraction from TOF-MRA images. Neurocomputing, 2013, 121: 392–400.

    Article  Google Scholar 

  19. Dearden R, Clancy D. Particle filters for real-time fault detection in planetary rovers. In Proc. the 13th International Workshop on Principles of Diagnosis, May 2002.

  20. Buades A, Coll B, Morel J M. A non-local algorithm for image denoising. In Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2005, pp.60-65.

  21. Manj´on J V, Coupé P, Buades A et al. New methods for MRI denoising based on sparseness and self-similarity.Medical Image Analysis, 2012, 16(1): 18–27.

  22. Coupé P, Yger P, Prima S et al. An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Transactions on Medical Imaging, 2008, 27(4): 425–441.

  23. Wiest-Daesslé N, Prima S, Coupé P et al. Rician noise removal by non-local means filtering for low signal-to-noise ratio MRI: Applications to DT-MRI. In Proc. the 11th MICCAI, September 2008, Part II, pp.171-179.

  24. Jin Q, Grama I, Liu Q. A non-local means filter for removing the poisson noise. arXiv Preprint, arXiv:1309.4151, 2013.

  25. Li S Z. Markov Random Field Modeling in Image Analysis. London: Springer, 2009.

  26. Besag J. Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society, Series B (Methodological), 1974, 36(2): 192–236.

  27. Geman S, Geman D. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984, 6(6): 721–741.

    Article  MATH  Google Scholar 

  28. El-Baz A, Gimel’Farb G, Falk R et al. A novel 3D joint Markov-Gibbs model for extracting blood vessels from PCMRA images. In Proc. the 12th MICCAI, Part II, September 2009, pp.943-950.

  29. Ruan S, Bloyet D, Revenu M et al. Cerebral magnetic resonance image segmentation using fuzzy Markov random fields. In Proc. IEEE International Symposium on Biomedical Imaging, July 2002, pp.237-240.

  30. Eberhart R C, Kennedy J. A new optimizer using particle swarm theory. In Proc. the 6th International Symposium on Micro Machine and Human Science, October 1995, pp.39-43.

  31. Kennedy J, Eberhart R. Particle swarm optimization. In Proc. IEEE International Conference on Neural Networks, Volumn 4, November 27-December 1, 1995, pp.1942-1948.

  32. Lu W Z, Fan H Y, Lo S M. Application of evolutionary neural network method in predicting pollutant levels in downtown area of Hong Kong. Neurocomputing, 2003, 51: 387–400.

    Article  Google Scholar 

  33. Da Y, Ge X R. An improved PSO-based ANN with simulated annealing technique. Neurocomputing, 2005, 63: 527–533.

    Article  Google Scholar 

  34. Juang C F. A hybrid genetic algorithm and particle swarm optimization for recurrent network design. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2004, 34(2): 997–1006.

    Article  Google Scholar 

  35. Tang Y G, Guan X P. Parameter estimation for time-delay chaotic system by particle swarm optimization. Chaos, Solitons and Fractals, 2009, 40(3): 1391–1398.

    Article  MathSciNet  MATH  Google Scholar 

  36. Lin C J, Lee C Y. Non-linear system control using a recurrent fuzzy neural network based on improved particle swarm optimisation. International Journal of Systems Science, 2010, 41(4): 381–395.

    Article  MathSciNet  MATH  Google Scholar 

  37. Rex D E, Shattuck D W, Woods R P et al. A metaalgorithm for brain extraction in MRI. NeuroImage, 2004, 23(2): 625–637.

    Article  Google Scholar 

  38. Jomier J, LeDigarcher V, Aylward S R. Comparison of vessel segmentations using STAPLE. In Proc. the 8th MICCAI, October 2005, pp.523-530.

  39. Bouix S, Martin-Fernandez M, Ungar L et al. On evaluating brain tissue classifiers without a ground truth. NeuroImage, 2007, 36(4): 1207–1224.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Information Science and Technology, Beijing Normal University, Beijing, 100875, China

    Rong-Fei Cao, Xing-Ce Wang, Zhong-Ke Wu & Ming-Quan Zhou

  2. Engineering Research Center of Virtual Reality and Applications, Ministry of Education, Beijing, 100875, China

    Rong-Fei Cao, Xing-Ce Wang, Zhong-Ke Wu & Ming-Quan Zhou

  3. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China

    Xin-Yu Liu

Authors
  1. Rong-Fei Cao
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  2. Xing-Ce Wang
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  3. Zhong-Ke Wu
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  4. Ming-Quan Zhou
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  5. Xin-Yu Liu
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Corresponding author

Correspondence to Xing-Ce Wang.

Additional information

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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Cao, RF., Wang, XC., Wu, ZK. et al. A Parallel Markov Cerebrovascular Segmentation Algorithm Based on Statistical Model. J. Comput. Sci. Technol. 31, 400–416 (2016). https://doi.org/10.1007/s11390-016-1634-6

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  • DOI: https://doi.org/10.1007/s11390-016-1634-6

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