• DocumentCode
    1446175
  • Title

    Dyslexia Diagnostics by 3-D Shape Analysis of the Corpus Callosum

  • Author

    Elnakib, Ahmed ; Casanova, Manuel F. ; Gimelrfarb, G. ; Switala, Andrew E. ; El-Baz, Ayman

  • Author_Institution
    Bioeng. Dept., Univ. of Louisville, Louisville, KY, USA
  • Volume
    16
  • Issue
    4
  • fYear
    2012
  • fDate
    7/1/2012 12:00:00 AM
  • Firstpage
    700
  • Lastpage
    708
  • Abstract
    Dyslexia severely impairs learning abilities; therefore, improved diagnostic methods are needed. Neuropathological studies have revealed an abnormal anatomy of the corpus callosum (CC) in dyslexic brains. We propose a new approach for the quantitative analysis of 3-D magnetic resonance images (MRI) of the brain that ensures a more accurate quantification of anatomical differences between the CC of dyslexic and control subjects. The proposed approach consists of three main processing steps: 1) segmenting the CC from a given 3-D MRI using the learned CC shape and visual appearance; 2) extracting the centerline of the CC; and 3) cylindrical mapping of the CC surface for its comparative analysis. Validation on 3-D simulated phantoms demonstrates the ability of the proposed approach to accurately detect the shape variability between two 3-D surfaces. Experimental results revealed significant differences (at the 95% confidence level) between 14 normal and 16 dyslexic subjects in all four anatomical divisions, i.e., splenium, rostrum, genu, and body of their CCs. Moreover, the initial classification results based on the centerline length and CC thickness suggest that the proposed shape analysis is a promising supplement to the current techniques for diagnosing dyslexia.
  • Keywords
    biomedical MRI; brain; feature extraction; image segmentation; medical disorders; medical image processing; neurophysiology; phantoms; shape measurement; 3-D CC segmentation; 3-D magnetic resonance images; 3-D shape analysis; 3-D simulated phantoms; MRI; brains; centerline extraction; corpus callosum; dyslexia; learning ability impairment; neuropathological studies; shape variability; Image segmentation; Magnetic resonance imaging; Phantoms; Shape; Solid modeling; Three dimensional displays; Visualization; Corpus callosum; diagnosis; dyslexia; modeling; shape analysis; Adolescent; Adult; Case-Control Studies; Computer Simulation; Corpus Callosum; Dyslexia; Humans; Image Interpretation, Computer-Assisted; Magnetic Resonance Imaging; Male; Models, Neurological; Phantoms, Imaging;
  • fLanguage
    English
  • Journal_Title
    Information Technology in Biomedicine, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1089-7771
  • Type

    jour

  • DOI
    10.1109/TITB.2012.2187302
  • Filename
    6151156