Next: Introduction
Evaluating Non-Rigid Registration without
Ground Truth
Roy S. Schestowitz,
Carole J. Twining,
Vladimir S. Petrovic,
Timothy F. Cootes,
William R. Crum,
and Christopher J. Taylor
![[*]](http://www.cs.manchester.ac.uk/software/latex2html/icons/footnote.png)
Abstract:
We present a generic method for assessing the quality of non-rigid
registration (NRR), that does not require ground truth, but
rather depends solely on the registered images. We consider the
case where NRR is applied to a set of images, providing a
dense correspondence between images. Given this correspondence, it
is possible to build a generative statistical model of appearance
variation for the set. We observe that the quality of the
resulting model will depend on the quality of the correspondence.
We define measures of model specificity and
generalisation that can be used to assess the quality of
the model and, hence, the quality of the correspondence from which
it is derived. The approach does not depend on the specifics of
the registration algorithm or the form of the model. We validate
the approach by measuring the change in model quality, as the
correspondence of an initially registered set of MR images of the
brain is progressively perturbed, and compare the results with
those obtained using a method based on the overlap of ground-truth
anatomical labels. We demonstrate that, not only is the proposed
approach capable of assessing NRR reliably without ground truth,
but that it also provides a more sensitive measure of
misregistration than the overlap-based approach. Finally we apply the new method to compare the performance of three different registration algorithms on a set of
MR images of the brain, demonstrating that the method is able to discriminate between different methods of registration in a practical setting.
Next: Introduction
Roy Schestowitz
2007-03-11