Using sparse parametrization of deformation fields as means to classification

Large Deformation Di eomorphic Metric Mapping is a powerful technique which has been used to quantify variations in anatomical structures in medical images. This allows us to compare various images within and across a populations of classes using the underlying deformation eld which maps each image with the representative images of the class. The deformation eld can be described by a low-dimensional control point parameterization. We investigate the potential of this low-dimensional parameterization in classi cation and study the e ect of the underlying classi er parameters on the classi cation accuracy.