Fuzzy C-Means Based Liver CT Image Segmentation with Optimum Number of Clusters

  • Abder-Rahman Ali
  • Micael Couceiro
  • Aboul Ella Hassanien
  • Mohamed F. Tolba
  • Václav Snášel
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 303)


In this paper, we investigate the effect of using an optimum number of clusters with Fuzzy C-Means clustering, for Liver CT image segmentation. The optimum number of clusters to be used was measured using the average silhouette value. The evaluation was carried out using the Jaccard index, in which we concluded that using the optimum number of clusters may not necessarily lead to the best segmentation results.


Segmentation fuzzy c-means clustering CT 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Annadurai, S., Shanmugalakshmi, R.: Fundamentals of Digital Image Processing. Dorling Kindersley, Delhi (2006)Google Scholar
  2. 2.
    Läthén, G.: Segmentation Methods for Digital Image Analysis: Blood Vessels, Multi-scale Filtering, and Level Set Methods. Linköping studies in science and technology, thesis no. 1434 (2010)Google Scholar
  3. 3.
    Huang, X., Tsechpenakis, G.: Medical Image Segmentation. In: Hristidis, V. (ed.) Information Discovery on Electronic Health Records, ch. 10. Chapman & Hall (2009)Google Scholar
  4. 4.
    Leeuwen, M., Noorddzij, J., Feldberg, M., Hennipman, A., Doornewaaard, H.: Focal Liver Lesions: Characterization with Triphasic Spiral CT. Radiology 201, 327–336 (1996)Google Scholar
  5. 5.
    Mharib, A., Ramli, A., Mashohor, S., Mahmood, R.: Survey on Liver CT Image Segmentation Methods. Artificial Intelligence Review 37(2), 83–95 (2012)CrossRefGoogle Scholar
  6. 6.
    Oliveira, J., Pedrycz, W.: Advances in Fuzzy Clustering and its Applications. John Wiley Sons Ltd., England (2007)CrossRefGoogle Scholar
  7. 7.
    Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. John Wiley Sons, Chichester (2001)zbMATHGoogle Scholar
  8. 8.
    Bezdek, J.: Fuzzy Mathematics in Pattern Classification. Ph.D. thesis, Applied Mathematic Center, Cornell University, Ithaca, NY (1973)Google Scholar
  9. 9.
    Cao, B., Wang, G., Chen, S., Guo, S.: Fuzzy Information and Engineering, vol. 1. Springer, Heidelberg (2010)zbMATHGoogle Scholar
  10. 10.
    Maji, P., Pal, S.: Maximum Class Separability for Rough-Fuzzy C-Means Based Brain MR Image Segmentation. T. Rough Sets 9, 114–134 (2008)Google Scholar
  11. 11.
    Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On Clustering Validation Techniques. Journal of Intelligent Information Systems 17, 107–145 (2001)CrossRefzbMATHGoogle Scholar
  12. 12.
    Rawashdeh, M., Ralescu, A.: Center-Wise Intra-Inter Silhouettes. In: Hüllermeier, E., Link, S., Fober, T., Seeger, B. (eds.) SUM 2012. LNCS, vol. 7520, pp. 406–419. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  13. 13.
    Rousseeuw, P.: Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis. Computational and Applied Mathematics 20, 53–65 (1987)CrossRefzbMATHGoogle Scholar
  14. 14.
    Anter, A., Azar, A., Hassanien, A., El-Bendary, N., ElSoud, M.: Automatic Computer Aided Segmentation for Liver and Hepatic Lesions Using Hybrid Segmentations Techniques. In: IEEE Proceedings of Federated Conference on Computer Science and Information Systems 2013, pp. 193–198 (2013)Google Scholar
  15. 15.
    Jaccard, P.: Etude Comparative de la Distribution Orale Dansune Portion des Alpes et des Jura. Bulletin del la Socit Vaudoise des Sciences Naturelles 37, 547–579 (1901)Google Scholar
  16. 16.
    Narayana, C., Sreenivasa Reddy, E., Seetharama Prasad, M.: Automatic Image Segmentation using Ultrafuzziness. International Journal of Computer Applications 49(12), 6–13 (2012)CrossRefGoogle Scholar
  17. 17.
    Aguiar, R., Sales, R.: Dependence Analysis of the Market Index Using Fuzzy C- Means Algorithm. In: International Proceedings of Economics Development & Research, vol. 1, pp. 362–365 (2011)Google Scholar
  18. 18.
    Wu, K.: Analysis of Parameter Selections for Fuzzy C-Means. Pattern Recognition 45, 407–415 (2012)CrossRefzbMATHGoogle Scholar
  19. 19.
    Bush, B.: Fuzzy Clustering Techniques: Fuzzy C-Means and Fuzzy Min-Max Clustering Neural Networks, (accessed on March 18, 2014) (retrieved)
  20. 20.
    Bezdek, J., Ehrlich, R., Full, W.: FCM: The Fuzzy C-Means Clustering Algorithm. Computers and Geosciences 10, 191–203 (1984)CrossRefGoogle Scholar
  21. 21.
    Abdel-Dayem, A., El-Sakka, M.: Fuzzy C-Means Clustering for Segmenting Carotid Artery Ultrasound Images. In: Kamel, M.S., Campilho, A. (eds.) ICIAR 2007. LNCS, vol. 4633, pp. 935–948. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  22. 22.
    Kannan, S.R.: A new segmentation system for brain MR images based on fuzzy techniques. Applied Soft Computing 8(4), 1599–1606 (2008)CrossRefGoogle Scholar
  23. 23.
    Zhou, H., Schaefer, G., Sadka, A.H., Celebi, M.E.: Anisotropic mean shift based fuzzy c-means segmentation of dermoscopy images. IEEE Journal of Selected Topics in Signal Processing 3(1), 26–34 (2009)CrossRefGoogle Scholar
  24. 24.
    Rawashdeh, M., Ralescu, A.: Crisp and Fuzzy Cluster Validity: Generalized Intra-Inter Silhouette Index. In: 2012 IEEE Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS), pp. 1–6 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Abder-Rahman Ali
    • 1
  • Micael Couceiro
    • 2
    • 3
  • Aboul Ella Hassanien
    • 1
    • 4
  • Mohamed F. Tolba
    • 5
  • Václav Snášel
    • 6
  1. 1.Scientific Research Group in Egypt (SRGE)CairoEgypt
  2. 2.Artificial Perception for Intelligent Systems and Robotics (AP4ISR), Institute of Systems and Robotics (ISR)University of CoimbraCoimbraPortugal
  3. 3.Ingeniarius, Lda.MealhadaPortugal
  4. 4.Faculty of Computers and InformationCairo UniversityCairoEgypt
  5. 5.Faculty of Computers and InformationAin Shams UniversityCairoEgypt
  6. 6.Electrical Engineering & Computer ScienceVSB-TUOstravaCzech Republic

Personalised recommendations