A minimum description length objective function for groupwise non-rigid image registration

Groupwise non-rigid registration aims to find a dense correspondence across a set of images, so that analogous structures in the images are aligned. For purely automatic inter-subject registration the meaning of correspondence should be derived purely from the available data (i.e., the full set of images), and can be considered as the problem of learning correspondences given the set of example images. We argue that the Minimum Description Length (MDL) approach is a suitable method of statistical inference for this problem, and we give a brief description of applying the MDL approach to transmitting both single images and sets of images, and show that the concept of a reference image (which is central to defining a consistent correspondence across a set of images) appears naturally as a valid model choice in the MDL approach. This paper provides a proof-of-concept for the construction of objective functions for image registration based on the MDL principle.

• Type: Journal
• Year: 2008
• Where: IVC
• Authors: Stephen Marsland, Carole J. Twining, Christopher J. Taylor
• Keywords: Groupwise Non-rigid Registration, Image, IVC 2008, MDL Approach