Application of Improved FCM Algorithm in Brain Image Segmentation

  • Manzhuo Yin
  • Jinghuan GuoEmail author
  • Yuankun Chen
  • Yong Mu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)


Aiming at the problems of fuzzy c-means clustering (FCM) and its improved algorithms for MRI image segmentation, this paper proposes a new FCM algorithm based on neighborhood pixel correlation. The algorithm works out the influence degree of the neighborhood pixels on the central pixel by the correlation of the gray-level difference between the domain pixel and the center pixel. Then, the distance between the neighborhood pixel and the cluster center is used to control the membership of the center pixel, the improved algorithm will solve the existing influence factors of unification, ignoring the difference between pixels, resulting in inaccuracy of segmentation results. At last, this algorithm is implemented by MATLAB tool and compared with FCMS and FLICM algorithms. The feasibility of the presented algorithm and the accuracy of the segmentation result are verified by evaluating the algorithm and the experimental results according to the relevant evaluation criteria.


Brain MRI image FCM improvement algorithm Pixel gray correlation Dissimilarity coefficient 



Fundamental Research Funds for the Central Universities (No. 3132018194). Project name: Research on Ship Scheduling Method Based on Swarm Intelligence Hybrid Optimization Algorithm.


  1. 1.
    Wang L, Chitiboi T, Meine H, Gnther M, Hahn HK. Principles and methods for automatic and semi-automatic tissue segmentation in mri data. Magnetic Resonance Materials in Physics Biology and Medicine. 2016;29(2):95–110.CrossRefGoogle Scholar
  2. 2.
    Makropoulos A, Gousias IS, Ledig C, Aljabar P, Serag A, Hajnal JV, Edwards AD, Counsell SJ, Rueckert D. Automatic whole brain mri segmentation of the developing neonatal brain. IEEE Transactions on Medical Imaging. 2014;33(9):1818–31.CrossRefGoogle Scholar
  3. 3.
    A. Roche and F. Forbes, “Partial volume estimation in brain mri revisited,” in International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 771–8, (2014).CrossRefGoogle Scholar
  4. 4.
    Iglesias JE, Sabuncu MR, Leemput KV. A unified framework for cross-modality multi-atlas segmentation of brain mri. Medical Image Analysis. 2013;17(8):1181.CrossRefGoogle Scholar
  5. 5.
    Namburu A, Samay SK, Edara SR. Soft fuzzy rough set-based mr brain image segmentation. Applied Soft Computing. 2016;54:CrossRefGoogle Scholar
  6. 6.
    Bezdek JC, Hall LO, Clarke LP. Review of mr image segmentation techniques using pattern recognition. Medical Physics. 1993;20(4):1033.CrossRefGoogle Scholar
  7. 7.
    N. A. Mohamed, M. N. Ahmed, and A. Farag, “Modified fuzzy c-mean in medical image segmentation,” in Engineering in Medicine and Biology Society, 1998. Proceedings of the International Conference of the IEEE, pp. 1377–1380 vol.3, (1999).Google Scholar
  8. 8.
    Lei X, Ouyang H. Image segmentation algorithm based on improved fuzzy clustering. Cluster Computing. 2018;62(1):1–11.Google Scholar
  9. 9.
    Zhang DQ, Chen SC. A novel kernelized fuzzy c-means algorithm with application in medical image segmentation. Artificial Intelligence in Medicine. 2004;32(1):37–50.CrossRefGoogle Scholar
  10. 10.
    Krinidis S, Chatzis V. A robust fuzzy local information c-means clustering algorithm. IEEE Transactions on Image Processing. 2010;19(5):1328–37.MathSciNetCrossRefGoogle Scholar
  11. 11.
    Cai W, Chen S, Zhang D. Fast and robust fuzzy c -means clustering algorithms incorporating local information for image segmentation. Pattern Recognition. 2007;40(3):825–38.CrossRefGoogle Scholar
  12. 12.
    Zhu L, Chung FL, Wang S. Generalized fuzzy c-means clustering algorithm with improved fuzzy partitions. IEEE Transactions on Systems Man and Cybernetics Part B Cybernetics A Publication of the IEEE Systems Man and Cybernetics Society. 2009;39(3):578–91.CrossRefGoogle Scholar
  13. 13.
    Pei HX, Zheng ZR, Wang C, Li CN, Shao YH. D-fcm: Density based fuzzy c-means clustering algorithm with application in medical image segmentation. Procedia Computer Science. 2017;122:407–14.CrossRefGoogle Scholar
  14. 14.
    Zhang X, Song L, Lei P. Improvement of flicm for image segmentation. Journal of Computational Information Systems. 2014;10(21):9429–36.Google Scholar
  15. 15.
    M. K. N. T. H. J. M. D. S. MM, “Weighted neighborhood pixels segmentation method for automated detection of cracks on pavement surface images,” Journal of Computing in Civil Engineering, vol. 30, no. 2, p. 04015021, (2016).CrossRefGoogle Scholar
  16. 16.
    Ding S, Du M, Sun T, Xu X, Xue Y. An entropy-based density peaks clustering algorithm for mixed type data employing fuzzy neighborhood. Knowledge-Based Systems. 2017;133:CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Manzhuo Yin
    • 1
  • Jinghuan Guo
    • 1
    Email author
  • Yuankun Chen
    • 1
  • Yong Mu
    • 1
  1. 1.College of Information Science and TechnologyDalian Maritime UniversityDalian CityChina

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