• DocumentCode
    3082518
  • Title

    Automated segmentation of spinal diffusion tensor MR imaging

  • Author

    Younis, Akmal A. ; Ramirez, Nelson ; Pattany, Pradip M. ; Burns, Robert J. ; Sharawy, Mohamed I.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Miami Univ., FL, USA
  • fYear
    2005
  • fDate
    8-10 April 2005
  • Firstpage
    187
  • Lastpage
    192
  • Abstract
    A novel automated segmentation technique is presented for the delineation of white matter and gray matter regions in diffusion tensor magnetic resonance imaging of the spine. The technique involves an automated method for the extraction of the spinal cord regions from the diffusion tensor imaging data and relies on the fuzzy means clustering approach, which is inherently robust. Experimental results obtained for the segmentation of in vitro spinal cord sections of varying ages from 48 to 80 years demonstrate the viability of the automated segmentation technique. Statistical comparison with manually delineated white matter regions indicates the potential of the automated technique for the investigation and analysis of white matter abnormalities in diffusion tensor magnetic resonance imaging of the spine.
  • Keywords
    biomedical MRI; fuzzy systems; image segmentation; medical image processing; neurophysiology; pattern clustering; statistical analysis; automated segmentation; diffusion tensor magnetic resonance imaging; fuzzy means clustering; spinal cord region extraction; spinal diffusion tensor MR imaging; spinal gray matter regions; spinal white matter regions; Data mining; Diffusion tensor imaging; Image analysis; Image segmentation; In vitro; Magnetic analysis; Magnetic resonance imaging; Robustness; Spinal cord; Tensile stress;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    SoutheastCon, 2005. Proceedings. IEEE
  • Print_ISBN
    0-7803-8865-8
  • Type

    conf

  • DOI
    10.1109/SECON.2005.1423243
  • Filename
    1423243