• DocumentCode
    1947637
  • Title

    Segmentation of the striatum using data fusion

  • Author

    Frenoux, Emmanuelle ; Barra, Vincent ; Boire, Jean-Yves

  • Author_Institution
    ERIM, Univ. de Clermont-Ferrand I, France
  • Volume
    3
  • fYear
    2001
  • fDate
    2001
  • Firstpage
    2630
  • Abstract
    Proposes a new segmentation scheme to detect cerebral structures in MRI acquisitions using numerical information contained in the image and expert knowledge brought by a specialist. This process is divided in three steps: first, information contained in the MR image is extracted using a fuzzy clustering algorithm, and theoretical information concerning the structure to segment is modeled using possibility theory. Information fusion is then processed, followed by a decision step ending the structure segmentation. Heads of caudate nuclei and putamens are segmented using this method. Results are promising and validation is performed using both numerical indexes and assessment by an expert. This method can be applied to any cerebral structure in an MR image, provided that it can be described in terms of shape, direction and distance by an expert and that the contrast and resolution of the MRI are sufficient.
  • Keywords
    biomedical MRI; brain; fuzzy logic; image resolution; image segmentation; medical image processing; sensor fusion; MR image; MRI acquisitions; caudate nuclei; cerebral structures; contrast; data fusion; decision step; direction; distance; expert knowledge; fuzzy clustering algorithm; numerical information; possibility theory; putamens; resolution; shape; striatum segmentation; structure segmentation; Biomedical imaging; Boring; Clustering algorithms; Data mining; Head; Image segmentation; Magnetic resonance imaging; Parkinson´s disease; Possibility theory; Shape;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE
  • ISSN
    1094-687X
  • Print_ISBN
    0-7803-7211-5
  • Type

    conf

  • DOI
    10.1109/IEMBS.2001.1017322
  • Filename
    1017322