• DocumentCode
    3073311
  • 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
  • fYear
    2009
  • fDate
    22-24 June 2009
  • Firstpage
    366
  • Lastpage
    369
  • 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;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Bioinformatics and BioEngineering, 2009. BIBE '09. Ninth IEEE International Conference on
  • Conference_Location
    Taichung
  • Print_ISBN
    978-0-7695-3656-9
  • Type

    conf

  • DOI
    10.1109/BIBE.2009.58
  • Filename
    5211244