Sparse Bayesian Registration
Loïc Le Folgoc, Hervé Delingette, Antonio Criminisi, Nicholas Ayache · Lecture notes in computer science · 2014
We propose a Sparse Bayesian framework for non-rigid registration. Our principled approach is flexible, in that it efficiently finds an optimal, sparse model to represent deformations among any preset, widely overcomplete range of basis functions. It addresses open challenges in state-of-the-art registration, such as the automatic joint estimate of model parameters ( e.g. noise and regularization levels). We demonstrate the feasibility and performance of our approach on cine MR, tagged MR and 3D US cardiac images, and show state-of-the-art results on benchmark datasets evaluating accuracy of motion and strain. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.