Groupwise Non-Rigid Registration: The Minimum Description Length Approach

Abstract

The principled non-rigid registration of groups of images requires a fully groupwise objective function. We consider the problem as one of finding the optimal dense correspondence between the images in the set, where optimality is defined using the Minimum Description Length (MDL) principle, that the transmission of a model of the data, together with the parameters of that model, should be as short as possible. We demonstrate that this approach provides a suitable objective function by applying it to the task of non-rigid registration of a set of 2D T1-weighted MR images of the human brain. Furthermore, we show that even in the case when substantial portions of the images are missing, the algorithm not only converges to the correct solution, but also allows meaningful integration of image data across the training set, allowing the original image to be reconstructed.