A new image analysis approach for automatic classification of autistic brains

Autism is a developmental disorder characterized by social deficits, impaired communication, and restricted and repetitive patterns of behavior. Recent neuropathological studies of autism have revealed abnormal anatomy of the cerebral white matter (CWM) in autistic brains. In this paper we introduced a novel approach to classify autistic from normal subjects based on a new shape analysis of cerebral white matter gyrifications for both normal and autistic subjects. The proposed shape analysis technique consists of three main steps. The first step is to segment cerebral white matter from proton density MRI images using a priorly learned visual appearance model for the 3D cerebral white matter in order to control the evolution of deformable boundaries. The appearance prior is modeled with a translation and rotation invariant Markov-Gibbs random field of voxel intensities with a pairwise interaction model. The second step is to extract the gyrifications of cerebral white matter from the segmented cerebral white matter. The last step is to perform shape analysis to quantify the thickness of the extracted cerebral white matter gyrifications for both autistic and normal subjects. The preliminary results of the proposed image analysis has yielded promising results that would, in the near future, supplement the use of current technologies for diagnosing autism.