• DocumentCode
    1587013
  • Title

    Ant-based clustering algorithm for magnetic resonance breast image segmentation

  • Author

    Moftah, Hossam M. ; Hassanien, Aboul Ella ; Alimi, Adel M. ; Karray, Hichem ; Tolba, M.F.

  • Author_Institution
    Fac. of Comput. & Inf., Beni Suef Univ., Beni Suef, Egypt
  • fYear
    2013
  • Firstpage
    161
  • Lastpage
    166
  • Abstract
    This article introduces an improved version of the ant-clustering approach for image segmentation. An application of breast cancer magnetic resonance breas imaging has been chosen and the improved ant-based clustering approach has been applied to see their ability and accuracy to isolate the region of interest in the MRI images. The aim of the proposed ant-based clustering is to identify target objects through an The experimental results obtained, show that the modified ant-based clustering is superior to the classical ant-based clustering and the overall accuracy offered by the improved approach confirm that the effectiveness and performance is 98% in average.
  • Keywords
    biomedical MRI; gynaecology; image segmentation; medical image processing; optimisation; pattern clustering; MRI images; ant-based clustering algorithm; breast cancer magnetic resonance breast imaging; magnetic resonance breast image segmentation; target objects; Accuracy; Algorithm design and analysis; Clustering algorithms; Image segmentation; Ant colony optimization; Segmentation; clustering; magnetic resonance images;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Hybrid Intelligent Systems (HIS), 2013 13th International Conference on
  • Conference_Location
    Gammarth
  • Print_ISBN
    978-1-4799-2438-7
  • Type

    conf

  • DOI
    10.1109/HIS.2013.6920475
  • Filename
    6920475