PSO-2S Optimization Algorithm for Brain MRI Segmentation

  • Abbas El Dor
  • Julien Lepagnot
  • Amir Nakib
  • Patrick Siarry
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 238)


In image processing, finding the optimal threshold(s) for an image with a multimodal histogram can be done by solving a Gaussian curve fitting problem, i.e. fitting a sum of Gaussian probability density functions to the image histogram. This problem can be expressed as a continuous nonlinear optimization problem. The goal of this paper is to show the relevance of using a recently proposed variant of the Particle Swarm Optimization (PSO) algorithm, called PSO-2S, to solve this image thresholding problem. PSO-2S is a multi-swarm PSO algorithm using charged particles in a partitioned search space for continuous optimization problems. The performances of PSO-2S are compared with those of SPSO-07 (Standard Particle Swarm Optimization in its 2007 version), using reference images, i.e. using test images commonly used in the literature on image segmentation, and test images generated from brain MRI simulations. The experimental results show that PSO-2S produces better results than SPSO-07 and improves significantly the stability of the segmentation method.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Abbas El Dor
    • 1
  • Julien Lepagnot
    • 2
  • Amir Nakib
    • 1
  • Patrick Siarry
    • 1
  1. 1.Laboratoire LISSI, E.A. 3956Université de Paris-Est CréteilVitry-sur-SeineFrance
  2. 2.Laboratoire LMIA, E.A. 3993Université de Haute-AlsaceMulhouseFrance

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