Consistent Information Content Estimation for Diffusion Tensor MR Images

We propose novel information content estimators for diffusion tensor images using binless approaches based on nearest-neighbour distances. Combining these estimators with existing tensor distance metrics allows us to generate entropy estimates that are consistent and accurate for diffusion tensor data. Further, we are able to obtain such estimators without having to reduce the dimensionality of the tensor data to the point where a binning estimator can be reliably used. We test our estimators in the context of noise estimation, image segmentation, and image registration. Results on 12 datasets from LBAM and 50 datasets from LONI show our estimators more accurately reflect the underlying DTI data and provide faster convergence rates for image segmentation and registration algorithms.