Abstract
The ill posedness of the image reconstruction problem requires approached solution as a regularization of a specific criterion, in general, a penalty is imposed on the solution. The challenge is to avoid the smoothing of edges which are very important attributes of the image when it is regularized. The x-ray Tomography is classified as sensing problems for which we do not know the equipment measurement transfer function so it is considered as an ill posed inverse problem. Many studies have been developed to solve this problem, among them the Bayesian inference which aims at smoothing artifact in image. The problem for Bayesian methods is the edge penalization. In this work, we first present a fuzzy inference model for the edge preservation. Under this condition, we show that it is possible to find the best global solution to the problem by introducing genetic algorithm optimization (GA).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
S.Y. Chun, Y.K. Dewaraja, J.A. Fessler, Alternating direction method of multiplier for tomography with non-local regularizers. IEEE Trans. Med. Imag 33(10), 1960–8 (2014)
A.M. Djafari, Joint Estimation of parameters and hyperparamaters in a Bayesian approach of solving inverse problems, in Proceedings of the International Conference on Image Processing, vol. II (Lausane, Suisse, 1996), pp. 473–477
A.M. Djafari, A Full Bayesian Approach for Inverse Problems (Kluwer Acadimic Publishers, New Mexico, 1996), pp. 135–143
A.M.T. Gouicem, K. Benmahammed, R. Drai, M. Yahi, A. Taleb-Ahmed, Multi-objective G-a optimization of fuzzy penalty for image reconstruction from projections in x-ray tomography. Digit. Signal Process. Elsevier 22(3), 486–596 (2012)
J.H. Holland, Outline for logical theory of adaptive systems. J. ACM 03, 297–314 (1962)
J. Ma, Q. Feng, Y. Feng, J. Huang, W. Chen, Generalized Gibbs priors based positron emission tomography reconstruction. Comput. Biol. Med. 40, 565–571 (2010). Elsevier
P.P. Mondal, K. Rajan, Iterative image reconstruction for emission tomography using fuzzy potential. IEEE Trans. Image Signal Process. (2005)
S.A. Qureshi, S.M. Mirza, M. Arif, Determination of optimal number of projections for parallel-ray transmission tomography using hybrid continuous genetic algorithm, in textit International Journal of Imaging Systems and Technology, in Process (2006)
S.N. Sivanandam, S.N. Deepa, Introduction to Genetic Algorithms (Springer, Berlin, 2008). ISBN 978-3-540-73189-4
D.V. Ville, M. Nachtegael, D.V. Weken, E.E. Kerre, W. Philips, I. Lemahieu, Noise reduction by fuzzy image filtering. IEEE Trans. Fuzzy Syst. 11, 429–435 (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer-Verlag GmbH Germany
About this chapter
Cite this chapter
Gouicem, A.M.T., Yahi, M., Taleb-Ahmed, A. (2017). Fuzzy Edge Detection in Computed Tomography Through Genetic Algorithm Optimization. In: Nakib, A., Talbi, EG. (eds) Metaheuristics for Medicine and Biology. Studies in Computational Intelligence, vol 704. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-54428-0_3
Download citation
DOI: https://doi.org/10.1007/978-3-662-54428-0_3
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-54426-6
Online ISBN: 978-3-662-54428-0
eBook Packages: EngineeringEngineering (R0)