Medical Image Segmentation Using GA-Based Modified Spatial FCM Clustering

  • Amiya HalderEmail author
  • Avranil Maity
  • Ananya Das
Part of the Studies in Computational Intelligence book series (SCI, volume 771)


This chapter proposes a unique method for unsupervised segmentation of medical images using genetic algorithm (GA)-based spatial fuzzy C-means (SFCM) clustering. The aim of the algorithm is to segment the medical image into an appropriate number of clusters, whereby the required number of clusters is computed automatically. SFCM takes into account the effect of neighborhood pixels on a central pixel, and thus the set of clusters obtained by SFCM forms the basis for a genetic algorithm where different genetic operators are used to further calibrate the centroids. A validity index is used to obtain the optimal number of clusters. The experimental results of the proposed method are compared with existing methods for further validation.


Image segmentation SFCM Genetic algorithm Medical image 


  1. 1.
    Pham, D.L., C. Xu, and J.L. Prince. 2000. Current methods in medical image segmentation. Annual Review of Biomedical Engineering 2 (1): 315–337.CrossRefGoogle Scholar
  2. 2.
    Na, S., L. Xumin, and G. Yong. 2010. Research on k-means clustering algorithm: An improved k-means clustering algorithm. In Third International Symposium on Intelligent Information Technology and Security Informatics, 63–67.Google Scholar
  3. 3.
    Wagstaff, K., C. Cardie, S. Rogers and S. Schrdl. 2001. Constrained k-means clustering with background knowledge. In ICML, vol. 1, 577–584.Google Scholar
  4. 4.
    Cai, W., S. Chen, and D. Zhang. 2007. Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recognition 40 (3): 825–838.CrossRefGoogle Scholar
  5. 5.
    Bezdek, J.C., R. Ehrlich, and W. Full. 1984. FCM: The fuzzy c-means clustering algorithm. Computers and Geosciences 10 (2): 191–203.CrossRefGoogle Scholar
  6. 6.
    Gamarra, Daniel Fernando Tello. 2015. Fuzzy image segmentation using validity indexes correlation. International Journal of Computer Science and Information Technology 7: 15–26.CrossRefGoogle Scholar
  7. 7.
    Kaur, P., A.K. Soni, and A. Gosain. 2013. A robust kernelized intuitionistic fuzzy c-means clustering algorithm in segmentation of noisy medical images. Pattern Recognition Letters 34 (2): 163–175.CrossRefGoogle Scholar
  8. 8.
    Ahmed, M.N., S.M. Yamany, N. Mohamed, A.A. Farag, and T. Moriarty. 2002. A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Transactions on Medical Imaging 21 (3): 193–199.CrossRefGoogle Scholar
  9. 9.
    Chuang, K., H. Tzeng, S. Chen, J. Wu, and T. Chen. 2006. Fuzzy c-means clustering with spatial information for image segmentation. Computerized Medical Imaging and Graphics 30 (1): 9–15.CrossRefGoogle Scholar
  10. 10.
    Kole, D.K., and Amiya Halder. 2010. An efficient image segmentation algorithm using dynamic GA based clustering. International Journal of Logistics and Supply Chain Management 2 (1): 17–20.Google Scholar
  11. 11.
    Halder, A., S. Pramanik, and A. Kar. 2011. Dynamic image segmentation using fuzzy c-means based genetic algorithm. International Journal of Computer Applications 28 (6): 15–20.CrossRefGoogle Scholar
  12. 12.
    Halder, A., and N. Pathak. 2011. An evolutionary dynamic clustering based colour image segmentation. International Journal of Image Processing 4 (6): 549–556.Google Scholar
  13. 13.
    Maji, P., and S.K. Pal. 2007. Rough set based generalized fuzzy C-means algorithm and quantitative indices. IEEE Transactions on Systems, Man, and Cybernetics Part B (Cybernetics) 37 (6): 1529–1540.CrossRefGoogle Scholar
  14. 14.
    Halder, A., and S. Guha. 2017. Rough kernelized fuzzy c-means based medical image segmentation. International Conference on Computational Intelligence, Communications, and Business Analytics, 466–474.Google Scholar
  15. 15.
    Bhattacharya, R.K. 2012. Introduction to genetic algorithms, IIT Guwahati 12.Google Scholar
  16. 16.
    Ghose. T. 2002. Optimization technique and an introduction to genetic algorithms and simulated annealing. In Proceedings of International workshop on Soft Computing and Systems, 1–19.Google Scholar
  17. 17.
  18. 18.
    Turi. R.H. 2001. Clustering-based colour image segmentation. PhD thesis, Monash University.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.STCETKolkataIndia

Personalised recommendations