Advertisement

Fuzzy C-Means Cluster Segmentation Algorithm Based on Modified Membership

  • Yanling Li
  • Gang Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5552)

Abstract

Fuzzy c-means (FCM) algorithm is one of the most popular methods for image segmentation. However, the standard FCM algorithm is noise sensitive because of not taking into account the spatial information in the image. In this paper, we present fuzzy c-means cluster segmentation algorithm based on modified membership (MFCMp,q) that incorporates spatial information into the membership function for clustering. The spatial function is the weighted summation of the membership function in the neighborhood of each pixel under consideration. The fast MFCMp,q algorithm(FMFCMp,q) which speeds up the convergence of MFCMp,q algorithm is achieved when the MFCMp,q algorithm is initialized by the fast fuzzy c-means algorithm based on statistical histogram. The experiments on the artificial synthetic image and real-world datasets show that MFCMp,q algorithm and FMFCMp,q algorithm can segment images more effectively and provide more robust segmentation results.

Keywords

Image segmentation Fuzzy c-means Spatial information Tatistical histogram 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bezdek, J.C., Hall, L.O., Clarke, L.P.: Review of MR Image Segmentation Techniques Using Pattern Recognition. Med. Phys. 20, 1033–1048 (1993)CrossRefGoogle Scholar
  2. 2.
    Pham, D.L., Xu, C.Y., Prince, J.L.: A Survey of Current Methods in Medical Image Segmentation. Annu. Rev. Biomed. Eng. 2, 315–337 (2000)CrossRefGoogle Scholar
  3. 3.
    Wells, W.M., et al.: Adaptive Segmentation of MRI Data. IEEE Trans. Med. Imag. 15, 429–442 (1996)CrossRefGoogle Scholar
  4. 4.
    Bezdek, J.C.: Pattern Recognition With Fuzzy Objective Function Algorithms, New York (1981)Google Scholar
  5. 5.
    Udupa, J.K., Samarasekera, S.: Fuzzy Connectedness and Object Definition: Theory, Algorithm and Applications in Image Segmentation. Graph. Models Image Process 58, 246–261 (1996)CrossRefGoogle Scholar
  6. 6.
    Pal, N., Pal, S.: A Review on Image Segmentation Techniques. Pattern Recognition 26, 1277–1294 (1993)CrossRefGoogle Scholar
  7. 7.
    Pham, D.L., Prince, J.L.: An Adaptive Fuzzy C-means Algorithm for Image Segmentation in the Presence of Intensity Inhomogeneities. Pattern Recognition Letters 20, 57–68 (1999)CrossRefzbMATHGoogle Scholar
  8. 8.
    Tolias, Y.A., Panas, S.M.: On Applying Spatial Constraints in Fuzzy Image Clustering Using a Fuzzy Rule-based System. IEEE Signal Process. Lett. 5, 245–247 (1998)CrossRefGoogle Scholar
  9. 9.
    Liew, A.W.C., Leung, S.H., Lau, W.H.: Fuzzy Image Clustering Iincorporating Spatial Continuity. Inst. Elec. Eng. Vis. Image Signal Process 147, 185–192 (2000)CrossRefGoogle Scholar
  10. 10.
    Hathaway, R.J., Bezdek, J.C.: Generalized Fuzzy C-means Clustering Strategies Using Lp Norm Distance. IEEE Trans. Fuzzy Syst. 8, 567–572 (2000)CrossRefGoogle Scholar
  11. 11.
    Jajuga, K.: L1 Norm Based Fuzzy Clustering. Fuzzy Sets Syst. 39, 43–50 (1991)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Leski, J.: An ε -insensitive Approach to Fuzzy Clustering. Int. J. Applicat. Math. Comp. Sci. 11, 993–1007 (2001)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Hou, Z., et al.: Regularized Fuzzy C-means Method for Brain Tissue Clustering. Pattern Recognition Letters 28, 1788–1794 (2007)CrossRefGoogle Scholar
  14. 14.
    Cai, W.L., Chen, S.C., Zhang, D.Q.: Fast and Robust Fuzzy C-means Clustering Algorithms Incorporating Local Information for Image Segmentation. Pattern Recognition 40, 825–838 (2007)CrossRefzbMATHGoogle Scholar
  15. 15.
    Ahmed, M.N., et al.: A Modified Fuzzy C-means Algorithm for Bias Field Estimation and Segmentation of MRI Data. IEEE Trans. Med. Imaging 21, 193–199 (2002)CrossRefGoogle Scholar
  16. 16.
    Pham, D.L.: Fuzzy Clustering with Spatial Constraints. In: IEEE Proceedings of the International Conference Image Processing, New York, pp. 65–68 (2002)Google Scholar
  17. 17.
    Chen, S.C., Zhang, D.Q.: Robust Image Segmentation Using FCM with Spatial Constraints Based on New Kernel-induced Distance Measure. IEEE Trans. Systems Man Cybernet. B 34, 1907–1916 (2004)CrossRefGoogle Scholar
  18. 18.
    Yang, Y., Zheng, C.X., Lin, P.: Fuzzy Clustering with Spatial Constraints for Image Thresholding. Optica Applicata 35, 309–315 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yanling Li
    • 1
    • 2
  • Gang Li
    • 2
  1. 1.Institute of System Engineering, Department of Control Science and EngineeringHuazhong University of Science and TechnologyWuhanChina
  2. 2.College of Computer and Information TechnologyXinyang Normal UniversityXinyangChina

Personalised recommendations