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Image segmentation method based on improved fuzzy Chan-Vese model

  • He JianweiEmail author
  • Pei Jiali
Article
  • 54 Downloads

Abstract

Medical image segmentation is a hot topic in the field medical image processing. The segmentation methods based on level set and the ones based on fuzzy set are currently very popular in the field of medical image segmentation. But these methods do not balance between global and local features of the image. This paper combines the advantages of these two methods, proposes a fuzzy Chan-Vese model, which introduces fuzzy clustering into Chan-Vese model. This model extends the regional energy part of Chan-Vese model to regional energy based on fuzzy clustering, meanwhile adds fuzzy cluster objects as the constraint of the model, so it can take account of global and local features of the image. In the medical image segmentation experiments, this paper uses OTSU method to execute initial segmentation for getting the initial segmentation curve, and then uses fuzzy Chan-Vese model to realize image segmentation. Experimental results show that, with the help of prior knowledge of segmentation prototypes of medical images, the proposed method has achieved very good segmentation results.

Keywords

Level set Chan-Vese model Medical image segmentation Fuzzy c-means OTSU 

References

  1. 1.
    Al-Ayyoub M, Abu-Dalo AM, Jararweh Y et al (2015) A gpu-based implementations of the fuzzy c-means algorithms for medical image segmentation[J]. J Supercomput 71(8):3149–3162CrossRefGoogle Scholar
  2. 2.
    Cheng L, Yang J, Fan X et al (2005) A generalized level set formulation of the Mumford-Shah functional for brain MR image segmentation[C]//biennial international conference on information processing in medical imaging. Springer, Berlin, pp 418–430Google Scholar
  3. 3.
    Gong M, Liang Y, Shi J, Ma W, Ma J (2013) Fuzzy c-means clustering with local information and kernel metric for image segmentation[J]. IEEE Trans Image Process 22(2):573–584MathSciNetCrossRefGoogle Scholar
  4. 4.
    Huo B, Li G, Yin F (2015) Medical and natural image segmentation algorithm using MF based optimization model and modified fuzzy clustering: a novel approach[J]. Int J Signal Process Image Process Pattern Recognit 8(7):223–234Google Scholar
  5. 5.
    Lee SH, Seo JK (2006) Level set-based bimodal segmentation with stationary global minimum[J]. IEEE Trans Image Process 15(9):2843–2852MathSciNetCrossRefGoogle Scholar
  6. 6.
    Li Y, Jiao L, Shang R, Stolkin R (2015) Dynamic-context cooperative quantum-behaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation[J]. Inf Sci 294:408–422MathSciNetCrossRefGoogle Scholar
  7. 7.
    Marquina A, Osher S (2000) Explicit algorithms for a new time dependent model based on level set motion for nonlinear deblurring and noise removal[J]. SIAM J Sci Comput 22(2):387–405MathSciNetCrossRefGoogle Scholar
  8. 8.
    Mumford D, Shah J (1989) Optimal approximations by piecewise smooth functions and associated variational problems[J]. Commun Pure Appl Math 42(5):577–685MathSciNetCrossRefGoogle Scholar
  9. 9.
    Narkhede HP (2013) Review of image segmentation techniques[J]. International journal of science and modern. Engineering 1(8):54–61Google Scholar
  10. 10.
    Norouzi A, Rahim MSM, Altameem A, Saba T, Rad AE, Rehman A, Uddin M (2014) Medical image segmentation methods, algorithms, and applications[J]. IETE Tech Rev 31(3):199–213CrossRefGoogle Scholar
  11. 11.
    Patil DD, Deore SG (2013) Medical image segmentation: a review[J]. Int J Comput Sci Mob Comput 2(1):22–27Google Scholar
  12. 12.
    Peng B, Zhang L, Zhang D (2013) A survey of graph theoretical approaches to image segmentation[J]. Pattern Recogn 46(3):1020–1038CrossRefGoogle Scholar
  13. 13.
    Torbati N, Ayatollahi A, Kermani A (2014) An efficient neural network based method for medical image segmentation[J]. Comput Biol Med 44:76–87CrossRefGoogle Scholar
  14. 14.
    Unnikrishnan R, Pantofaru C, Hebert M (2007) Toward objective evaluation of image segmentation algorithms[J]. IEEE Trans Pattern Anal Mach Intell 29(6):929–944CrossRefGoogle Scholar
  15. 15.
    Vala HJ, Baxi A (2013) A review on Otsu image segmentation algorithm[J]. Int J Adv Res Comput Eng Technol 2(2):387–389Google Scholar
  16. 16.
    Wang XF, Huang DS, Xu H (2010) An efficient local Chan–Vese model for image segmentation[J]. Pattern Recogn 43(3):603–618CrossRefGoogle Scholar
  17. 17.
    Yang X, Gao X, Tao D et al (2015) An efficient MRF embedded level set method for image segmentation[J]. IEEE Trans Image Process 24(1):9–21MathSciNetCrossRefGoogle Scholar
  18. 18.
    Zhang W, Li R, Deng H, Wang L, Lin W, Ji S, Shen D (2015) Deep convolutional neural networks for multi-modality isointense infant brain image segmentation[J]. NeuroImage 108:214–224CrossRefGoogle Scholar
  19. 19.
    Zheng Y, Jeon B, Xu D et al Image segmentation by generalized hierarchical fuzzy C-means algorithm[J]. Journal of IntelligentGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Information EngineeringNingbo Dahongying UniversityNingboChina

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