Autism diagnostics by 3D texture analysis of cerebral white matter gyrifications

  • Authors:

  • Ayman El-Baz;Manuel F. Casanova;Georgy Gimel'farb;Meghan Mott;Andrew E. Switala

  • Affiliations:

  • Bioengineering Department, University of Louisville, Louisville, KY;Department of Psychiatry and Behavioral Science, University of Louisville;Department of Computer Science, University of Auckland, New Zealand;Department of Psychiatry and Behavioral Science, University of Louisville;Department of Psychiatry and Behavioral Science, University of Louisville

  • Venue:
  • MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
  • Year:
  • 2007

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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 cerebral white matter (CWM) in autistic brains. We explore a possibility of distinguishing between autistic and normal brains by a quantitative shape analysis of CWM gyrifications on 3D proton density MRI (PD-MRI) images. Our approach consists of (i) segmentation of the CWM on a 3D brain image using a deformable 3D boundary; (ii) extraction of gyrifications from the segmented CWM, and (iii) shape analysis to quantify thickness of the extracted gyrifications and classify autistic and normal subjects. The boundary evolution is controlled by two probabilistic models of visual appearance of 3D CWM: the learned prior and the current appearance model. Initial experimental results suggest that the proposed 3D texture analysis is a promising supplement to the current techniques for diagnosing autism.