The Way of Improving PSO Performance: Medical Imaging Watermarking Case Study
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.
KeywordsGenetic Algorithm Particle Swarm Optimization Image Watermark Watermark Scheme Host Image
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- 1.Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press (1975)Google Scholar
- 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.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
- 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
- 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.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.Premalatha, K., Natarajan, A.M.: Hybrid PSO and GA for Global Maximization. Int. J. Open Problems Compt. Math. 2(4) (2009)Google Scholar