Chapter

Applications of Intelligent Optimization in Biology and Medicine

Volume 96 of the series Intelligent Systems Reference Library pp 233-250

Date:

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

  • Abder-Rahman AliAffiliated withScientific Research Group in Egypt (SRGE) Email author 
  • , Micael CouceiroAffiliated withInstitute of Systems and Robotics, Polo II, Pinhal de Marrocos, University of CoimbraRoboCorp, Polytechnic Institute of Coimbra, DEE, Rua Pedro Nunes
  • , Ahmed AnterAffiliated withScientific Research Group in Egypt (SRGE)Faculty of Computers and Information, Computer Science Department, Mansoura University
  • , Aboul-Ella HassanienAffiliated withScientific Research Group in Egypt (SRGE)Faculty of Computers and Information, Computer Science Department, Cairo University

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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