• DocumentCode
    2622764
  • Title

    An improved fuzzy clustering approach using possibilist c-means algorithm: Application to medical image MRI

  • Author

    El harchaoui, Nour-eddine ; Bara, Samir ; Kerroum, Mounir Ait ; Hammouch, Ahmed ; Ouaddou, Mohamed ; Aboutajdine, Driss

  • Author_Institution
    LRIT, Mohamed V-Agdal Univ., Rabat, Morocco
  • fYear
    2012
  • fDate
    22-24 Oct. 2012
  • Firstpage
    117
  • Lastpage
    122
  • Abstract
    Currently, the MRI brain image processing is a vast area of research, several methods and approaches have been used to segment these images (thresholding, region, contour, clustering). In this work, we propose a novel segmentation approach, which is based on fuzzy c-means clustering and using possibilist c-means approach. To validate our approach, we have tested successfully on several datasets of real images MRI. Thus, to show the performance of our method, we compared our results with different segmentation algorithms: k-means, fuzzy c-means, and possibilist c-means.
  • Keywords
    biomedical MRI; brain; fuzzy set theory; image segmentation; medical image processing; pattern clustering; brain image processing; fuzzy clustering; image segmentation; medical image MRI; possibilist c-means algorithm; Biomedical imaging; Clustering algorithms; Computational modeling; Computers; Image segmentation; Magnetic resonance imaging; Phase change materials; Clustering; Fuzzy cmeans; Image MRI; K-means; Possibilist c-means; Segmentation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Science and Technology (CIST), 2012 Colloquium in
  • Conference_Location
    Fez
  • Print_ISBN
    978-1-4673-2726-8
  • Electronic_ISBN
    978-1-4673-2724-4
  • Type

    conf

  • DOI
    10.1109/CIST.2012.6388074
  • Filename
    6388074