Image Segmentation Using Variable Kernel Fuzzy C Means (VKFCM) Clustering on Modified Level Set Method

  • Tara Saikumar
  • Khaja FasiUddin
  • B. Venkata Reddy
  • Md. Ameen Uddin
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 131)


In this paper, Variable Kernel Fuzzy C-Means (VKFCM) was used to generate an initial contour curve which overcomes leaking at the boundary during the curve propagation. Firstly, VKFCM algorithm computes the fuzzy membership values for each pixel. On the basis of VKFCM the edge indicator function was redefined. Using the edge indicator function the image segmentation of a medical image was performed to extract the regions of interest for further processing. The above process of segmentation showed a considerable improvement in the evolution of the level set function.


Image segmentation VKFCM Level set method 


  1. 1.
    Bezdek JC (1981) Pattern recognition with fuzzy objective function algorthims. Plenum Press, New YorkCrossRefGoogle Scholar
  2. 2.
    Wu KL, Yang MS (2002) Alternative c-means clustering algorithms. Pattern Recognit 35(10):2267–2278MATHCrossRefGoogle Scholar
  3. 3.
    Osher S, Sethian JA (1988) Fronts propagating with curvature dependent speed: algorthim’s based on the Hamilton-Jacobi formulation. J Comput Phys 79(1):12–49MathSciNetMATHCrossRefGoogle Scholar
  4. 4.
    Malladi R, Sethain J, Vemuri B (1995) Shape modeling with front propagation: a level set approach. IEEE Trans Pattern Anal Mach Intell 17(2):158–175CrossRefGoogle Scholar
  5. 5.
    Staib L, Zeng X, Schultz R, Duncan J (2000) Shape constraints in deformable models. In: Bankman IN (ed) Handbook of medical imaging. Academic Press, New York, pp 147–157CrossRefGoogle Scholar
  6. 6.
    Leventon M, Faugeraus O, Grimson W (2000) Wells W (2000) Level set based segmentation with intensity and curvature priors. Workshop on mathematical methods in biomedical image analysis proceedings, In, pp 4–11Google Scholar
  7. 7.
    Paragios N, Deriche R (2000) Geodesic active contours and level sets for the detection and tracking of moving objects. IEEE Trans pattern Anal Mach Intell 22:266–280CrossRefGoogle Scholar
  8. 8.
    Vese LA, Chan TF (2002) A multiphase level set frame wor for image segmentation using the mumford and shah model. Int J Comput Vis 50(3):271–293MATHCrossRefGoogle Scholar
  9. 9.
    Shi Y, Karl WC (2005) Real-time tracking using level set. In: Proceedings of IEEE computer society conference on computer vision and, pattern recognition, vol 2, pp 34–42Google Scholar
  10. 10.
    Li C, Xu C, Gui C et al (2005) Level set evolution without re-initialization: a new varitional formulation. IEEE computer society conference on computer vision and pattern recognition, In, pp 430–436Google Scholar
  11. 11.
    Sethain I (1999) Level set methods and fast marching methods. Cambridge University Press, CambridgeGoogle Scholar
  12. 12.
    Dunn JC (1973) A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J Cybern 3:32–57MathSciNetMATHCrossRefGoogle Scholar
  13. 13.
    Bezedek J (1980) A convergence thheorem for the fuzzy ISODATA clustering algorthims. IEEE Trans Pattern Anal Mach Intell 2:78–82Google Scholar
  14. 14.
    Zhang L, Zhou WD, Jiao LC (2002) Kernel clustering algorithm (in chinese). Chin J Comput 25(6):587–590Google Scholar
  15. 15.
    Osher S, Fedkiw R (2002) Level set methods and dynamic implicit surfaces. Springer, New York, pp 112–113Google Scholar
  16. 16.
    Peng D, Merrimam B, Osher S, Zhao H, Kang M (1996) A PDE-based fast local level set method. J Comp Phys 155:410–438CrossRefGoogle Scholar
  17. 17.
    Gomes J, Faugeras O (2000) Reconciling distance functions and level sets. J Vis Commun Image Represent 11(2):209–223CrossRefGoogle Scholar
  18. 18.
    Mcinerney T, Terzopouls D (1996) Deformable models in medical image analysis: a survey. Med Image Anal 1(2):9l–108CrossRefGoogle Scholar
  19. 19.
    Dao-Qiang Z, Song C (2003) Clustering in completed data usig Kernel-based fuzzy c-means algorthim. Neural Process Lett 18(3):155–162CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Tara Saikumar
    • 1
  • Khaja FasiUddin
    • 2
  • B. Venkata Reddy
    • 3
  • Md. Ameen Uddin
    • 1
  1. 1.Department of ECECMR Technical CampusHyderabadIndia
  2. 2.Department of ECEVITSKarimnagarIndia
  3. 3.Department of ECEGNITSHyderabadIndia

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