Title :
Segmentation of Brain MR Images Using an Ant Colony Optimization Algorithm
Author :
Lee, Myung-Eun ; Kim, Soo-Hyung ; Cho, Wan-Hyun ; Park, Soon-Young ; Lim, Jun-Sik
Author_Institution :
Dept. of Comput. Sci., Chonnam Nat. Univ., Gwangju, South Korea
Abstract :
In this paper, we describe a segmentation method for brain MR images using an ant colony optimization (ACO) algorithm. This is a relatively new meta-heuristic algorithm and a successful paradigm of all the algorithms which take advantage of the insectpsilas behavior. It has been applied to solve many optimization problems with good discretion, parallel, robustness and positive feedback. As an advanced optimization algorithm, only recently, researchers began to apply ACO to image processing tasks. Hence, we segment the MR brain image using ant colony optimization algorithm. Compared to traditional meta-heuristic segmentation methods, the proposed method has advantages that it can effectively segment the fine details.
Keywords :
biomedical MRI; brain; image segmentation; medical image processing; optimisation; ant colony optimization algorithm; brain MR images; image processing; image segmentation; meta-heuristic algorithm; Alzheimer´s disease; Ant colony optimization; Bioinformatics; Biomedical engineering; Brain; Computer science; Image processing; Image segmentation; Noise reduction; Statistics; MR brain image; ant colony optimization; meta-heuristic algorithm; segmentation;
Conference_Titel :
Bioinformatics and BioEngineering, 2009. BIBE '09. Ninth IEEE International Conference on
Conference_Location :
Taichung
Print_ISBN :
978-0-7695-3656-9
DOI :
10.1109/BIBE.2009.58