  Diffusion magnetic resonance imaging, a non-invasive tool to infer white
matter fiber connections, produces a large number of streamlines containing a
wealth of information on structural connectivity. The size of these
tractography outputs makes further analyses complex, creating a need for
methods to group streamlines into meaningful bundles. In this work, we address
this by proposing a set of kernel dictionary learning and sparsity priors based
methods. Proposed frameworks include L-0 norm, group sparsity, as well as
manifold regularization prior. The proposed methods allow streamlines to be
assigned to more than one bundle, making it more robust to overlapping bundles
and inter-subject variations. We evaluate the performance of our method on a
labeled set and data from Human Connectome Project. Results highlight the
ability of our method to group streamlines into plausible bundles and
illustrate the impact of sparsity priors on the performance of the proposed
methods.
