Level Set and Region Based Surface Propagation for Diffusion Tensor MRI Segmentation

Di usion Tensor Imaging (DTI) is a relatively new modality for human brain imaging. During the last years, this modality has become widely used in medical studies. Tractography is currently the favorite technique to characterize and analyse the structure of the brain white matter. Only a few studies have been proposed to group data of particular interest. Rather than working on extracted bers or on an estimated scalar value accounting for anisotropy as done in other approaches, we propose to extend classical segmentation techniques based on surface evolution by considering region statistics de ned on the full di usion tensor eld itself. A multivariate Gaussian is used to approximate the density of the components of di usion tensor for each sub-region of the volume. We validate our approach on synthetic data and we show promising results on the extraction of the corpus callosum from a real dataset.