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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
BrainWeb: Simulated Brain Database, http://brainweb.bic.mni.mcgill.ca/brainweb (2012)
Clerc, M., et al.: The Particle Swarm Central website (2012), http://www.particleswarm.info
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)
Conway, J., Sloane, N.: Sphere Packings, Lattices and Groups. Springer (1999)
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)
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)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall, Inc., Upper Saddle River (2006)
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)
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)
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)
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)
Nakib, A., Oulhadj, H., Siarry, P.: Non-supervised image segmentation based on multiobjective optimization. Pattern Recognition Letters 29(2), 161–172 (2008)
Pitas, I.: Digital Image Processing Algorithms and Applications. John Wiley & Sons (2000)
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)
Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging 13, 146–165 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
El Dor, A., Lepagnot, J., Nakib, A., Siarry, P. (2014). PSO-2S Optimization Algorithm for Brain MRI Segmentation. In: Pan, JS., Krömer, P., Snášel, V. (eds) Genetic and Evolutionary Computing. Advances in Intelligent Systems and Computing, vol 238. Springer, Cham. https://doi.org/10.1007/978-3-319-01796-9_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-01796-9_2
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01795-2
Online ISBN: 978-3-319-01796-9
eBook Packages: EngineeringEngineering (R0)