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)

Abstract

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    BrainWeb: Simulated Brain Database, http://brainweb.bic.mni.mcgill.ca/brainweb (2012)
  2. 2.
    Clerc, M., et al.: The Particle Swarm Central website (2012), http://www.particleswarm.info
  3. 3.
    Collins, D.L., Zijdenbos, A.P., Kollokian, V., Sled, J.G., Kabani, N.J., Holmes, C.J., Evans, A.C.: Design and construction of a realistic digital brain phantom. IEEE Transactions on Medical Imaging 17(3), 463–468 (1998)CrossRefGoogle Scholar
  4. 4.
    Conway, J., Sloane, N.: Sphere Packings, Lattices and Groups. Springer (1999)Google Scholar
  5. 5.
    El Dor, A., Clerc, M., Siarry, P.: A multi-swarm PSO using charged particles in a partitioned search space for continuous optimization. Computational Optimization and Applications 53(1), 271–295 (2012)MathSciNetCrossRefMATHGoogle Scholar
  6. 6.
    Feng, H.-M., Horng, J.-H., Jou, S.-M.: Bacterial Foraging Particle Swarm Optimization Algorithm Based Fuzzy-VQ Compression Systems. Journal of Information Hiding and Multimedia Signal Processing 3(3), 227–239 (2012)Google Scholar
  7. 7.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall, Inc., Upper Saddle River (2006)Google Scholar
  8. 8.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: The IEEE International Conference on Neural Networks IV, Perth, Australia, November 27-December 1, pp. 1942–1948 (1995)Google Scholar
  9. 9.
    Kwok, N.M., Wang, D., Ha, Q.P., Fang, G., Chen, S.Y.: Locally-Equalized Image Contrast Enhancement Using PSO-Tuned Sectorized Equalization. In: Chatterjee, A., Siarry, P. (eds.) Computational Intelligence in Image Processing, pp. 21–36. Springer (2013)Google Scholar
  10. 10.
    Lee, S.U., Chung, S.Y., Park, R.H.: A comparative performance study of several global thresholding techniques for segmentation. Computer Vision, Graphics, and Image Processing 52(2), 171–190 (1990)CrossRefGoogle Scholar
  11. 11.
    Lepagnot, J., Nakib, A., Oulhadj, H., Siarry, P.: A new multiagent algorithm for dynamic continuous optimization. International Journal of Applied Metaheuristic Computing 1(1), 16–38 (2010)CrossRefGoogle Scholar
  12. 12.
    Nakib, A., Oulhadj, H., Siarry, P.: Non-supervised image segmentation based on multiobjective optimization. Pattern Recognition Letters 29(2), 161–172 (2008)CrossRefGoogle Scholar
  13. 13.
    Pitas, I.: Digital Image Processing Algorithms and Applications. John Wiley & Sons (2000)Google Scholar
  14. 14.
    Sahoo, P.K., Soltani, S., Wong, A.K.C., Chen, Y.C.: A survey of thresholding techniques. Comput. Vision Graph. Image Process. 41(2), 233–260 (1988)CrossRefGoogle Scholar
  15. 15.
    Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging 13, 146–165 (2004)CrossRefGoogle Scholar

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

Personalised recommendations