• DocumentCode
    1710573
  • Title

    A modified Fuzzy C-means algorithm with symmetry information for MR brain image segmentation

  • Author

    Jayasuriya, Surani Anuradha ; Liew, Alan Wee-Chung

  • Author_Institution
    Sch. of Inf. & Commun. Technol., Griffith Univ., Gold Coast, QLD, Australia
  • fYear
    2013
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    In this paper, we present a novel modified Fuzzy C-means algorithm with symmetry information to reduce the effect of noise in brain tissue segmentation in magnetic resonance image (MRI). We integrate brain´s bilateral symmetry into the conventional Fuzzy C-means (FCM) as an additional term. In experiments, some synthetic images, and both simulated and real brain images were used to investigate the robustness of the method against noise. Finally, the method was compared with the conventional FCM algorithm. Results show the viability of the approach and the preliminary investigation appears promising.
  • Keywords
    biological tissues; biomedical MRI; brain; fuzzy set theory; image denoising; image segmentation; medical image processing; pattern clustering; FCM algorithm; MR brain image segmentation; brain bilateral symmetry; brain tissue segmentation; magnetic resonance image; modified fuzzy c-means algorithm; noise effect reduction; symmetry information; synthetic images; Brain; Clustering algorithms; Image segmentation; Linear programming; Magnetic resonance imaging; Noise; Standards; Brain image segmentation; Brain symmetry; Fuzzy c-means; MRI; Mid-sagittal plane;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information, Communications and Signal Processing (ICICS) 2013 9th International Conference on
  • Conference_Location
    Tainan
  • Print_ISBN
    978-1-4799-0433-4
  • Type

    conf

  • DOI
    10.1109/ICICS.2013.6782786
  • Filename
    6782786