Bayesian framework for white matter fibers similarity measure

We provide a Bayesian framework for measuring similarity in white matter fiber bundles based on Gaussian processes. This framework does not rely on point-to-point correspondences, it takes into account a priori information about the fiber structure working with three dimensional curves instead of point sequences. Moreover, it spans an inner product space among curves together with its induced metric. Thus, it provides an environment to perform statistics on curves. Finally, we show clustering results to illustrate the utility of this model.