Autism diagnostics by centerline-based shape analysis of the Corpus Callosum

Autism severely impairs personal behavior and communication skills, so that improved diagnostic methods are called for. Neuropathological studies have revealed abnormal anatomy of the Corpus Callosum (CC) in autistic brains. We explore a possibility of distinguishing between autistic and normal (control) brains by quantitative CC shape analysis in the 3D magnetic resonance images (MRI). Our approach consists of the three 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) classifying the subject as autistic or normal based on the estimated length of the centerline of the CC using a k-Nearest neighbor classifier. Experiments revealed significant differences (at the 95% confidence level) between the CC centerlines for 17 normal and 17 autistic subjects. Our initial classification suggests the proposed centerline-based shape analysis of the CC is a promising supplement to the current autism diagnostics.