PSO-2S Optimization Algorithm for Brain MRI Segmentation
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.
Unable to display preview. Download preview PDF.
- 1.BrainWeb: Simulated Brain Database, http://brainweb.bic.mni.mcgill.ca/brainweb (2012)
- 2.Clerc, M., et al.: The Particle Swarm Central website (2012), http://www.particleswarm.info
- 4.Conway, J., Sloane, N.: Sphere Packings, Lattices and Groups. Springer (1999)Google Scholar
- 6.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)Google Scholar
- 7.Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall, Inc., Upper Saddle River (2006)Google Scholar
- 8.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)Google Scholar
- 9.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)Google Scholar
- 13.Pitas, I.: Digital Image Processing Algorithms and Applications. John Wiley & Sons (2000)Google Scholar