• DocumentCode
    3071419
  • Title

    Computer-aided detection of depression from magnetic resonance images

  • Author

    Kipli, Kuryati ; Kouzani, Abbas Z. ; Joordens, Matthew

  • Author_Institution
    Sch. of Eng., Deakin Univ., Waurn Ponds, VIC, Australia
  • fYear
    2012
  • fDate
    1-4 July 2012
  • Firstpage
    500
  • Lastpage
    505
  • Abstract
    Magnetic resonance imaging (MRI) of the brain is used to detect depression disorder. However, a large number of MRI scans needs to be analyzed for such detection. Manual segmentation of the biomarkers in MRI scans by clinical experts can become time consuming and sometimes erroneous. This paper presents a study on computer-aided detection of depression from MRI scans. These systems have not yet been identified, categorized and compared in the literature. The paper covers fully automated to semi-automated detection systems. It also presents performance comparison for the considered systems.
  • Keywords
    biomedical MRI; brain; computer aided analysis; image segmentation; medical disorders; medical image processing; neurophysiology; psychology; MRI; biomarker segmentation; brain; computer aided detection; depression disorder; fully automated detection systems; magnetic resonance images; semi-automated detection systems; Biomedical imaging; Brain modeling; Computational modeling; Image segmentation; Magnetic resonance imaging; Manuals; Sensitivity; Depression; computer aided detection; magnetic resonance images;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Complex Medical Engineering (CME), 2012 ICME International Conference on
  • Conference_Location
    Kobe
  • Print_ISBN
    978-1-4673-1617-0
  • Type

    conf

  • DOI
    10.1109/ICCME.2012.6275745
  • Filename
    6275745