Frequency-domain analysis of the human brain for studies of autism

Geometric analysis of normal and autistic human subjects reveal distinctions in deformations in the corpus callosum (CC) that may be used for image analysis-based studies of autism. Preliminary studies showed that the CC of autistic patients is quite distinct from normal controls. We use an implicit vector representation of CC to carry out the registration process which reduces the pose differences between the CC's models. Then the complex Fourier descriptor analysis is used to extract a feature vector of each CC model. This feature is used to build a criteria of discrimination between the normal and autistic subjects. This paper introduces a new method for the 2D shape registration problem by matching vector distance functions. A variational frame work is proposed for the global and local registration of CC's. A gradient descent optimization is used which can efficiently handle both the rigid and the non-rigid operations together. The registration of real CC extracted from MRI data sets demonstrates the potential of the proposed approach. Discrimination results will be demonstrated as well to show the efficiency of the discrimination technique.