Particle Swarm Optimization Based Fast Fuzzy C-Means Clustering for Liver CT Segmentation

  • Abder-Rahman Ali
  • Micael Couceiro
  • Ahmed Anter
  • Aboul-Ella Hassanien
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 96)

Abstract

A Fast Fuzzy C-Means (FFCM) clustering algorithm, optimized by the Particle Swarm Optimization (PSO) method, referred to as PSOFFCM, has been introduced and applied on liver CT images. Compared to FFCM, the proposed approach leads to higher values in terms of Jaccard Index and Dice Coefficient, and thus, indicating higher similarity with the ground truth provided. Based on ANOVA analysis, PSOFFCM showed better results in terms of Dice Coefficient. It also showed better mean values in terms of Jaccard Index and Dice Coefficient based on the box and whisker plots.

Keywords

Fuzzy C-means Segmentation Liver CT 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Abder-Rahman Ali
    • 1
  • Micael Couceiro
    • 2
    • 3
  • Ahmed Anter
    • 1
    • 4
  • Aboul-Ella Hassanien
    • 1
    • 5
  1. 1.Scientific Research Group in Egypt (SRGE)CairoEgypt
  2. 2.Institute of Systems and Robotics, Polo II, Pinhal de MarrocosUniversity of CoimbraCoimbraPortugal
  3. 3.RoboCorp, Polytechnic Institute of Coimbra, DEE, Rua Pedro NunesCoimbraPortugal
  4. 4.Faculty of Computers and Information, Computer Science DepartmentMansoura UniversityMansouraEgypt
  5. 5.Faculty of Computers and Information, Computer Science DepartmentCairo UniversityGizaEgypt

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