• DocumentCode
    636299
  • Title

    Unsupervised segmentation of brain regions with similar microstructural properties: Application to alcoholism

  • Author

    Cosa, Alejandro ; Canals, Santiago ; Valles-Lluch, A. ; Moratal, David

  • Author_Institution
    Inst. de Neurociencias, Univ. Miguel Hernandez de Elche, San Juan, Spain
  • fYear
    2013
  • fDate
    3-7 July 2013
  • Firstpage
    1053
  • Lastpage
    1056
  • Abstract
    In this work, a novel brain MRI segmentation approach evaluates microstructural differences between groups. Going further from the traditional segmentation of brain tissues (white matter -WM-, gray matter -GM- and cerebrospinal fluid -CSF- or a mixture of them), a new way to classify brain areas is proposed using their microstructural MR properties. Eight rats were studied using the proposed methodology identifying regions which present microstructural differences as a consequence on one month of hard alcohol consumption. Differences in relaxation times of the tissues have been found in different brain regions (p<;0.05). Furthermore, these changes allowed the automatic classification of the animals based on their drinking history (hit rate of 93.75 % of the cases).
  • Keywords
    biological tissues; biomedical MRI; brain; image classification; image segmentation; medical disorders; medical image processing; neurophysiology; alcoholism application; automatic classification; brain MRI segmentation approach; brain areas; brain regions; brain tissues; hard alcohol consumption; microstructural MR properties; microstructural differences; traditional segmentation; unsupervised segmentation; Alcoholic beverages; Alcoholism; Animals; Brain modeling; Covariance matrices; Image segmentation; Magnetic resonance imaging; Alcoholism; Animals; Bayes Theorem; Brain; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; Male; Rats;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE
  • Conference_Location
    Osaka
  • ISSN
    1557-170X
  • Type

    conf

  • DOI
    10.1109/EMBC.2013.6609685
  • Filename
    6609685