Abstract
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.
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References
Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press (1975)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. VI, pp. 1942–1948 (1995)
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)
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)
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)
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)
Sedighizadeh, D., Masehian, E.: Particle Swarm Optimization Methods, Taxonomy and Applications. International Journal of Computer Theory and Engineering 1(5), 486–502 (2009)
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)
Premalatha, K., Natarajan, A.M.: Hybrid PSO and GA for Global Maximization. Int. J. Open Problems Compt. Math. 2(4) (2009)
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Soliman, M.M., Hassanien, A.E., Onsi, H.M. (2012). The Way of Improving PSO Performance: Medical Imaging Watermarking Case Study. In: Yao, J., et al. Rough Sets and Current Trends in Computing. RSCTC 2012. Lecture Notes in Computer Science(), vol 7413. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32115-3_28
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DOI: https://doi.org/10.1007/978-3-642-32115-3_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32114-6
Online ISBN: 978-3-642-32115-3
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