• DocumentCode
    2573623
  • Title

    Population intensity outliers or a new model for brain WM abnormalities

  • Author

    Tomas-Fernandez, Xavier ; Warfield, Simon K.

  • Author_Institution
    Comput. Radiol. Lab., Childrens´ Hosp. Boston, Boston, MA, USA
  • fYear
    2012
  • fDate
    2-5 May 2012
  • Firstpage
    1543
  • Lastpage
    1546
  • Abstract
    We present a new automatic method for segmentation of Multiple Sclerosis (MS) lesions in Magnetic Resonance Images. The algorithm performs tissue classification combining a within subject global tissue intensity model and a local tissue intensity model derived from an aligned set of healthy reference subjects. MS lesions are detected as outliers towards the proposed coupled global/local intensity model. Evaluation using BrainWeb synthetic, show our new coupled local/global intensity GMM model to be sensitive towards MS lesions, as well to be robust to noise and intensity inhomogeneity artifacts found MRI scans.
  • Keywords
    biological tissues; biomedical MRI; brain; cellular biophysics; diseases; image denoising; image segmentation; medical disorders; medical image processing; neurophysiology; physiological models; MRI scans; automatic method; brain WM abnormalities; brainWeb synthetic; global tissue intensity model; intensity inhomogeneity artifacts; local tissue intensity model; local-global intensity GMM model; magnetic resonance image segmentation; multiple sclerosis lesions; noise; population intensity outliers; tissue classification; Brain modeling; Image segmentation; Lesions; Magnetic resonance imaging; Noise; Nonhomogeneous media; Bayesian; Magnetic Resonance Imaging; Multiple Sclerosis; Segmentation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on
  • Conference_Location
    Barcelona
  • ISSN
    1945-7928
  • Print_ISBN
    978-1-4577-1857-1
  • Type

    conf

  • DOI
    10.1109/ISBI.2012.6235867
  • Filename
    6235867