BMVC 2004, Kingston, 7th-9th Sept, 2004

Groupwise Non-Rigid Registration: The Minimum Description Length Approach
C. J. Twining (University of Manchester), S. Marsland (Massey
University, N.Z.) and C. Taylor (University of Manchester)

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.
(pdf article)