Image-based detection of corpus callosum variability for more accurate discrimination between dyslexic and normal brains

Authors:
Ahmed Elnakib;Ayman EI-Baz;Manuel F. Casanova;Georgy Gimel'farb;Andrew E. Switala
Affiliations:
BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY;BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY;Department of Psychiatry and Behavioral Science, University of Louisville, Louisville, KY;Department of Computer Science, University of Auckland, Auckland, New Zealand;Department of Psychiatry and Behavioral Science, University of Louisville, Louisville, KY
Venue:
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Year:
2010

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Abstract

Dyslexia severely impairs learning abilities of children, so that 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 to quantitative analysis of three-dimensional (3D) magnetic resonance images (MRI) of the brain that ensures a more accurate quantification of anatomical differences between the CC of dyslexic and normal subjects. It consists of three main processing steps: (i) segmenting the CC from a given 3D MRI using the learned CC shape and visual appearance; (ii) extracting a centerline of the CC; and (iii) cylindrical mapping of the CC surface for its comparative analysis. Our experiments revealed significant differences (at the 95% confidence level) between 14 normal and 16 dyslexic subjects in four anatomical divisions, i.e. splenium, rostrum, genu and body of their CCs.