PSilhOuette: Towards an Optimal Number of Clusters Using a Nested Particle Swarm Approach for Liver CT Image Segmentation

  • Abder-Rahman Ali
  • Micael S. Couceiro
  • Aboul Ella Hassenian
Part of the Communications in Computer and Information Science book series (CCIS, volume 488)

Abstract

This paper proposes a nested particle swarm optimization (PSO) method to find the optimal number of clusters for segmenting a grayscale image. The proposed approach, herein denoted as PSilhOuette, comprises two hierarchically divided PSOs to solve two dependent problems: i) to find the most adequate number of clusters considering the silhouette index as a measure of similarity; and ii) to segment the image using the Fuzzy C-Means (FCM) approach with the number of clusters previously retrieved. Experimental results show that parent particles converge towards maximizing the silhouette value while, at the same time, child particles strive to minimize the FCM objective function.

Keywords

segmentation fuzzy c-means CT 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Abder-Rahman Ali
    • 1
  • Micael S. Couceiro
    • 2
    • 3
  • Aboul Ella Hassenian
    • 1
    • 4
  1. 1.Scientific Research Group in Egypt (SRGE)Egypt
  2. 2.Artificial Perception for Intelligent Systems and Robotics (AP4ISR), Institute of Systems and Robotics (ISR)University of Coimbra, Pinhal de Marrocos, Polo IICoimbraPortugal
  3. 3.Ingeniarius, Lda.MealhadaPortugal
  4. 4.Faculty of Computers and Information, Computer Science DepartmentCairo UniversityEgypt

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