• DocumentCode
    3412187
  • Title

    Feature space region growing

  • Author

    Revol-Muller, C. ; Grenier, T. ; Ting Li ; Benoit-Cattin, H.

  • Author_Institution
    CREATIS, Univ. de Lyon 1, Lyon, France
  • fYear
    2012
  • fDate
    Sept. 30 2012-Oct. 3 2012
  • Firstpage
    2585
  • Lastpage
    2588
  • Abstract
    We propose a framework for the segmentation by region growing approach leveraging on feature space. It has the advantages to deal with multidimensional data and easily specify locally adaptive segmentation. It relies upon the definition of a robust neighborhood which drives the region growing. We propose two applications to illustrate this framework: a segmentation of physical parameters maps of MRI by using n-dimensional region growing and a segmentation of highly noisy image by using adaptive region growing.
  • Keywords
    feature extraction; image segmentation; magnetic resonance imaging; MRI; adaptive region growing approach segmentation; feature space region; multidimensional data; n-dimensional region; noisy image segmentation; physical parameters maps segmentation; robust neighborhood; Estimation; Image segmentation; Indexes; Kernel; Magnetic resonance imaging; Noise measurement; Robustness; Feature space; Region growing; Segmentation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image Processing (ICIP), 2012 19th IEEE International Conference on
  • Conference_Location
    Orlando, FL
  • ISSN
    1522-4880
  • Print_ISBN
    978-1-4673-2534-9
  • Electronic_ISBN
    1522-4880
  • Type

    conf

  • DOI
    10.1109/ICIP.2012.6467427
  • Filename
    6467427