Accurate Automated Detection of Autism Related Corpus Callosum Abnormalities

Authors:
Ayman El-Baz;Ahmed Elnakib;Manuel F. Casanova;Georgy Gimel'Farb;Andrew E. Switala;Desha Jordan;Sabrina Rainey
Affiliations:
BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, USA;BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, USA;Department of Psychiatry and Behavioral Science, University of Louisville, Louisville, USA;Department of Computer Science, University of Auckland, Auckland, New Zealand;Department of Psychiatry and Behavioral Science, University of Louisville, Louisville, USA;BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, USA;BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, USA
Venue:
Journal of Medical Systems
Year:
2011

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Abstract

The importance of accurate early diagnostics of autism that severely affects personal behavior and communication skills cannot be overstated. Neuropathological studies have revealed an abnormal anatomy of the Corpus Callosum (CC) in autistic 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 autistic 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 17 normal and 17 autistic subjects in four anatomical divisions, i.e. splenium, rostrum, genu and body of their CCs.