Multimedia Tools and Applications

, Volume 76, Issue 6, pp 7869–7895 | Cite as

Improved fuzzy clustering algorithm with non-local information for image segmentation

  • Xiaofeng Zhang
  • Yujuan Sun
  • Gang Wang
  • Qiang Guo
  • Caiming Zhang
  • Beijing Chen
Article

Abstract

Fuzzy C-means(FCM) has been adopted to perform image segmentation due to its simplicity and efficiency. Nevertheless it is sensitive to noise and other image artifacts because of not considering spatial information. Up to now, a series of improved FCM algorithms have been proposed, including fuzzy local information C-means clustering algorithm(FLICM). In FLICM, one fuzzy factor is introduced as a fuzzy local similarity measure, which can control the trade-off between noise and details. However, the fuzzy factor in FLICM cannot estimate the damping extent of neighboring pixels accurately, which will result in poor performance in images of high-level noise. Aiming at solving this problem, this paper proposes an improved fuzzy clustering algorithm, which introduces pixel relevance into the fuzzy factor and could estimate the damping extent accurately. As a result, non-local context information can be utilized in the improved algorithm, which can improve the performance in restraining image artifacts. Experimental results on synthetic, medical and natural images show that the proposed algorithm performs better than current improved algorithms.

Keywords

Fuzzy clustering Image segmentation FLICM Pixel relevance Non-local information 

References

  1. 1.
    Ahmed MN, Yamany SM, Mohamed N, Farag AA (2002) A modified fuzzy C-mean algorithm for bias field estimation and segmentation of MRI data. IEEE Trans Med Imaging 21(3):193–199CrossRefGoogle Scholar
  2. 2.
    Besser H (1990) Visual access to visual images: the UC Berkeley Image Database Project. Library Trends 38(4):787–798Google Scholar
  3. 3.
    Bezdek J (1975) Mathematical models for systematics and taxonomy[C] 3:143–166. Proceedings of eighth international conference on numerical taxonomyGoogle Scholar
  4. 4.
    Bezdek J (1975) Cluster validity with fuzzy sets. J Cybern 3(3):58–73MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Bezdek J (1980) A convergence theorem for the fuzzy ISODATA clustering algorithms. IEEE Trans Pattern Anal Mach Intell 2(1):1–8CrossRefMATHGoogle Scholar
  6. 6.
    Bezdek J (1981) Pattern recognition with fuzzy objective function algorithms. Plenum, New YorkCrossRefMATHGoogle Scholar
  7. 7.
    Bezdek J, Hall L, Clarke L (1992) Review of MR image segmentation techniques using pattern recognition. Medical physics 20(4):1033–1048CrossRefGoogle Scholar
  8. 8.
    Cai W, Chen S, Zhang D (2007) Fast and robust fuzzy C-means clustering algorithms incorporating local information for image segmentation. Pattern Recogn 40:825–838CrossRefMATHGoogle Scholar
  9. 9.
    Chen S, Zhang D (2004) Robust image segmentation using fcm with spatial constraints based on new kernel-induced distance measure. IEEE Trans Syst Man Cybern Part B Cybern 34:1907–1916CrossRefGoogle Scholar
  10. 10.
    Cocosco C, Kollokian V, Kwan R et al BrainWeb: Online interface to a 3D MRI simulated brain database[Online]. Available: http://www.bic.mni.mcgill.ca/brainweb/
  11. 11.
    Dunn J (1974) A fuzzy relative of the ISODATA process and its use in detecting compact well separated clusters. J. Cybern 3:32–57MathSciNetCrossRefMATHGoogle Scholar
  12. 12.
    Gong M, Liang Y, Shi J, Ma W, Ma J (2013) Fuzzy C-means clustering with local information and kernel metric for image segmentation. IEEE Trans Image Process 22(2):573–584MathSciNetCrossRefGoogle Scholar
  13. 13.
    Gong M, Zhou Z, Ma J (2012) Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering. IEEE Trans Image Process 21(4):2141–2151MathSciNetCrossRefGoogle Scholar
  14. 14.
    Ji Z, Sun Q, Xia D (2010) A modified possibilistic fuzzy C-means clustering algorithm for bias field estimation and segmentation of brain MR image. Comput Med Imaging Graph 35:383–397CrossRefGoogle Scholar
  15. 15.
    Ji Z, Sun Q, Xia D (2011) A framework with modified fast FCM for brain MR images segmentation. Pattern Recogn 44:999–1013CrossRefGoogle Scholar
  16. 16.
    Jian M, Lam K, Dong J, Shen L (2015) Visual-patch- attention-aware Saliency Detection. IEEE Transactions on Cybernetics 45(8):1575–1586CrossRefGoogle Scholar
  17. 17.
    Krinidis S, Chatzis V (2010) A Robust Fuzzy Local Information C-means Clustering Algorithm. IEEE Trans Image Process 19(5):1328–1337MathSciNetCrossRefGoogle Scholar
  18. 18.
    Li J, Li X, Yang B, Sun X (2015) Segmentation-based Image Copy-move Forgery Detection Scheme. IEEE Trans Inf Forensics Secur 10(3):507–518CrossRefGoogle Scholar
  19. 19.
    MathWorks Image Processing Toolbox, Natick, MA[Online]. Available: http://www.mathworks.com/matlabcentral/fileexchange/14237
  20. 20.
    Pham D (2001) Robust fuzzy segmentation of magnetic resonance images. In: Proceedings of the 14th IEEE Symposium on Computer-Based Medical Systems, pp 127–131Google Scholar
  21. 21.
    Pham D (2001) Spatial models for fuzzy clustering. Comput Vis Image Underst 84(2):285–297CrossRefMATHGoogle Scholar
  22. 22.
    Pham D, Prince J (1999) An adaptive fuzzy C-means algorithm for image segmentation in the presence of intensity inhomogeneities. Pattern Recogn Lett 20 (1):57–68CrossRefMATHGoogle Scholar
  23. 23.
    Roy S, Agarwal H, Carass A, Bai Y, Pham D (2008) J Prince. Fuzzy C-means with variable compactness. 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro:452–455Google Scholar
  24. 24.
    Sun Y, Dong J, Jian M et al (2015) Fast 3D face reconstruction based on uncalibrated photometric stereo. Multimedia Tools and Applications 74(11):3635–3650CrossRefGoogle Scholar
  25. 25.
    Szilágyi L, Benyó Z, Szilágyii SM, Adam HS (2003) MR Brain image segmentation using an enhanced fuzzy C-means algorithm. In: Proceeding of 25th Annual International Conference of IEEE EMBS, pp 17–21Google Scholar
  26. 26.
    Wang G, Zhang X, Su Q et al (2015) A novel cortical thickness estimation method based on volumetric Laplace-Beltrami operator and heat kernel. Med Image Anal 22:1–20CrossRefGoogle Scholar
  27. 27.
    Zhang X, Wang G, Su Q et al An improved fuzzy algorithm for image segmentation using peak detection, spatial information and reallocation. Soft Comput. doi:10.1007/s00500-015-1920-1
  28. 28.
    Zhao F (2013) Fuzzy clustering algorithms with self-tuning non-local spatial information for image segmentation. Neurocomputing 106:115–125CrossRefGoogle Scholar
  29. 29.
    Zhao F, Jiao L, Liu H (2011) Fuzzy C-means clustering with non local spatial information for noisy image segmentation. Frontiers of Computer Science in China 5 (1):45–56MathSciNetCrossRefMATHGoogle Scholar
  30. 30.
    Zheng Y, Jeon B, Xu D, Jonathan W, Zhang H (2015) Image segmentation by generalized hierarchical fuzzy C-means algorithm. J Intell Fuzzy Syst 28(2):961–973Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Xiaofeng Zhang
    • 1
    • 3
  • Yujuan Sun
    • 1
  • Gang Wang
    • 1
  • Qiang Guo
    • 2
    • 3
  • Caiming Zhang
    • 2
    • 3
  • Beijing Chen
    • 4
  1. 1.School of Information and Electrical EngineeringLudong UniversityYantaiChina
  2. 2.School of Computer Science and TechnologyShandong University of Finance and EconomicsJinanChina
  3. 3.Shandong Provincial Key Laboratory of Digital Media TechnologyJinanChina
  4. 4.School of Computer & SoftwareNanjing University of Information Science & TechnologyNanjingChina

Personalised recommendations