Skip to main content
Springer Nature Link
Log in

Application of Improved FCM Algorithm in Brain Image Segmentation

  • Conference paper
  • First Online:
Communications, Signal Processing, and Systems (CSPS 2018)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 516))

  • 2381 Accesses

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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).

    Chapter  Google Scholar 

  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.

    Article  Google Scholar 

  5. Namburu A, Samay SK, Edara SR. Soft fuzzy rough set-based mr brain image segmentation. Applied Soft Computing. 2016;54:

    Article  Google Scholar 

  6. Bezdek JC, Hall LO, Clarke LP. Review of mr image segmentation techniques using pattern recognition. Medical Physics. 1993;20(4):1033.

    Article  Google Scholar 

  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. Lei X, Ouyang H. Image segmentation algorithm based on improved fuzzy clustering. Cluster Computing. 2018;62(1):1–11.

    Google Scholar 

  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.

    Article  Google Scholar 

  10. Krinidis S, Chatzis V. A robust fuzzy local information c-means clustering algorithm. IEEE Transactions on Image Processing. 2010;19(5):1328–37.

    Article  MathSciNet  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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).

    Article  Google Scholar 

  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:

    Article  Google Scholar 

Download references

Acknowledgments

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

Author information

Authors and Affiliations

  1. College of Information Science and Technology, Dalian Maritime University, Dalian City, Liaoning Province, China

    Manzhuo Yin, Jinghuan Guo, Yuankun Chen & Yong Mu

Authors
  1. Manzhuo Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jinghuan Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuankun Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yong Mu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jinghuan Guo .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA

    Qilian Liang

  2. School of Information and Communication Engineering, Dalian University of Technology, Dalian, China

    Xin Liu

  3. School of Information Science and Technology, Dalian Maritime University, Dalian, China

    Zhenyu Na

  4. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Wei Wang

  5. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Jiasong Mu

  6. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Baoju Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yin, M., Guo, J., Chen, Y., Mu, Y. (2020). Application of Improved FCM Algorithm in Brain Image Segmentation. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-13-6504-1_4

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-6504-1_4

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6503-4

  • Online ISBN: 978-981-13-6504-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics