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
Conference paper

DOI: 10.1007/978-3-319-08156-4_14

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 303)
Cite this paper as:
Ali AR., Couceiro M., Hassanien A.E., Tolba M.F., Snášel V. (2014) Fuzzy C-Means Based Liver CT Image Segmentation with Optimum Number of Clusters. In: Kömer P., Abraham A., Snášel V. (eds) Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014. Advances in Intelligent Systems and Computing, vol 303. Springer, Cham

Abstract

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.

Keywords

Segmentation fuzzy c-means clustering CT 

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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

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