CLASSIFICATION TECHNIQUES FOR AUTISTIC VS. TYPICALLY DEVELOPING BRAIN USING MRI DATA
Autism is a neurodevelopmental disorder that disrupts social and cognitive functions. Various autism studies revealed abnormalities in several brain regions. There is an increasing agreement from structural imaging studies on the abnormal anatomy of the white matter (WM) in autistic brains. In addition, the deficits in the size of the corpus callosum (CC) and its sub-regions in patients with autism relative to controls are well established. This paper presents two novel classification techniques of autism based on structural MRI. Our analysis is based on shape descriptions and geometric models. We compute the 3D distance map to describe the shape of the WM and use it as a statistical feature to discriminate between the two groups. We also use our recently proposed non-rigid registration technique to devise another classification approach by statistically analyzing and comparing the deformation fields generated from registering CC's onto each others. The accuracy of our techniques was tested on postmortem and on in vivo brain MR data. The results are very promising and show that, contrary to traditional methods, the proposed techniques are less sensitive to age and volume effects