The Way of Improving PSO Performance: Medical Imaging Watermarking Case Study

  • Mona M. Soliman
  • Aboul Ella Hassanien
  • Hoda M. Onsi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7413)


Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) are population based heuristic search techniques which can be used to solve the optimization problems modeled on the concept of evolutionary approach. In this paper we incorporate PSO with GA in hybrid technique called GPSO. This paper proposes the use of GPSO in designing an adaptive medical watermarking algorithm. Such algorithm aim to enhance the security, confidentiality , and integrity of medical images transmitted through the Internet. The experimental results show that the proposed algorithm yields a watermark which is invisible to human eyes and is robust against a wide variety of common attacks.


Genetic Algorithm Particle Swarm Optimization Image Watermark Watermark Scheme Host Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press (1975)Google Scholar
  2. 2.
    Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. VI, pp. 1942–1948 (1995)Google Scholar
  3. 3.
    Yang, B., Chen, Y., Zhao, Z.: A hybrid Evolutionary Algorithm by Combination of PSO and GA for Unconstrained and Constrained Optimization Problems. In: IEEE International Conference on Control and Automation, Guangzhou, China, pp. 166–170 (2007)Google Scholar
  4. 4.
    Fakhari, P., Vahedi, E., Lucas, C.: Protecting Patient Privacy From Unauthorized Release of Medical Images Using a Bio-nspired Wavelet-based Watermarking Approach. Digital Signal Processing 21, 433–446 (2011)CrossRefGoogle Scholar
  5. 5.
    Soliman, M.M., Ghali, N.I., Hassanien, A.E., Onsi, H.M.: An Adaptive Watermarking Approach for Medical Imaging Using Swarm Intelligent. International Journal of Smart Home 6(1), 37–51 (2012)Google Scholar
  6. 6.
    Juang, C.F.: A Hybrid of Genetic Algorithm and Particle Swarm Optimization for Recurrent Network Design. IEEE Transactions on Systems, Man, and Cypernetics Part B: Cybernetics 34(2), 997–1006 (2004)CrossRefGoogle Scholar
  7. 7.
    Sedighizadeh, D., Masehian, E.: Particle Swarm Optimization Methods, Taxonomy and Applications. International Journal of Computer Theory and Engineering 1(5), 486–502 (2009)Google Scholar
  8. 8.
    Pant, M., Thangaraj, R., Abraham, A.: Particle Swarm Optimization: Performance Tuning and Empirical Analysis. In: Abraham, A., Hassanien, A.-E., Siarry, P., Engelbrecht, A. (eds.) Foundations of Computational Intelligence Volume 3. SCI, vol. 203, pp. 101–128. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  9. 9.
    Premalatha, K., Natarajan, A.M.: Hybrid PSO and GA for Global Maximization. Int. J. Open Problems Compt. Math. 2(4) (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Mona M. Soliman
    • 1
  • Aboul Ella Hassanien
    • 1
  • Hoda M. Onsi
    • 1
  1. 1.Faculty of Computers and InformationCairo University, Scientific Research Group in Egypt (SRGE)CairoEgypt

Personalised recommendations