• DocumentCode
    420343
  • Title

    Application of rough set based dynamic parameter optimization to MRI segmentation

  • Author

    Widz, S. ; Slezak, Dominik ; Revett, K.

  • Author_Institution
    Dept. of Robotics & Multi-Agent Syst., Polish-Japanese Inst. of Inf., Warsaw, Poland
  • Volume
    1
  • fYear
    2004
  • fDate
    27-30 June 2004
  • Firstpage
    440
  • Abstract
    We introduce a multi-spectral MRI segmentation technique based on approximate reducts derived from the data mining paradigm of the theory of rough sets. We use genetic algorithms to tune the parameterized attributes and search for the best segmentation models based on approximate reducts.
  • Keywords
    approximation theory; biomedical MRI; data mining; genetic algorithms; image segmentation; rough set theory; approximate reducts; data mining; dynamic parameter optimization; genetic algorithms; multispectral MRI segmentation; parameterized attributes; rough set theory; segmentation models; Alzheimer´s disease; Brain; Computer science; Genetic algorithms; Histograms; Image segmentation; Magnetic resonance imaging; Parkinson´s disease; Rough sets; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Information, 2004. Processing NAFIPS '04. IEEE Annual Meeting of the
  • Print_ISBN
    0-7803-8376-1
  • Type

    conf

  • DOI
    10.1109/NAFIPS.2004.1336323
  • Filename
    1336323
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=420343