Region Segmentation from MR Brain Image Using an Ant Colony Optimization Algorithm


KIPS Transactions on Software and Data Engineering, Vol. 16, No. 3, pp. 195-202, Mar. 2009
10.3745/KIPSTB.2009.16.3.195, Full Text:

Abstract

In this paper, we propose the regions segmentation method of the white matter and the gray matter for brain MR image by using the ant colony optimization algorithm. Ant Colony Optimization (ACO) is a new meta heuristics algorithm to solve hard combinatorial optimization problem. This algorithm finds the expected pixel for image as the real ant finds the food from nest to food source. Then ants deposit pheromone on the pixels, and the pheromone will affect the motion of next ants. At each iteration step, ants will change their positions in the image according to the transition rule. Finally, we can obtain the segmentation results through analyzing the pheromone distribution in the image. We compared the proposed method with other threshold methods, viz. the Otsu' method, the genetic algorithm, the fuzzy method, and the original ant colony optimization algorithm. From comparison results, the proposed method is more exact than other threshold methods for the segmentation of specific region structures in MR brain image.


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Cite this article
[IEEE Style]
M. E. Lee, S. H. Kim and J. S. Lim, "Region Segmentation from MR Brain Image Using an Ant Colony Optimization Algorithm," KIPS Journal B (2001 ~ 2012) , vol. 16, no. 3, pp. 195-202, 2009. DOI: 10.3745/KIPSTB.2009.16.3.195.

[ACM Style]
Myung Eun Lee, Soo Hyung Kim, and Jun Sik Lim. 2009. Region Segmentation from MR Brain Image Using an Ant Colony Optimization Algorithm. KIPS Journal B (2001 ~ 2012) , 16, 3, (2009), 195-202. DOI: 10.3745/KIPSTB.2009.16.3.195.