• DocumentCode
    2168677
  • Title

    Segmentation of MR osteosarcoma images

  • Author

    Pan, Jincheng ; Li, Minglu

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Shanghai Jiao Tong Univ., China
  • fYear
    2003
  • fDate
    27-30 Sept. 2003
  • Firstpage
    379
  • Lastpage
    384
  • Abstract
    There is a large body of literature about MR image segmentation methods. In this paper we briefly review these methods, particular emphasis is based on the relative merits of single image versus multispectral segmentation, and supervised versus unsupervised segmentation methods. Finally, we discuss that how to segment osteosarcoma into tumor tissue classes based on three different MR weighted image parameters (T1, PD, and T2) using unsupervised fuzzy c-means (FCM) clustering algorithm technique for pattern recognition.
  • Keywords
    fuzzy set theory; image segmentation; magnetic resonance imaging; medical image processing; pattern clustering; pattern recognition; FCM; MR image segmentation; MR osteosarcoma image; fuzzy c-means clustering algorithm; multispectral segmentation; pattern recognition; single image; Biomedical imaging; Clustering algorithms; Computed tomography; Computer science; Data mining; Image edge detection; Image segmentation; Magnetic resonance imaging; Neoplasms; Pattern recognition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Multimedia Applications, 2003. ICCIMA 2003. Proceedings. Fifth International Conference on
  • Print_ISBN
    0-7695-1957-1
  • Type

    conf

  • DOI
    10.1109/ICCIMA.2003.1238155
  • Filename
    1238155