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Data-Driven Evaluation of Non-Rigid Registration via Appearance Modelling

Roy S. Schestowitz, Carole J. Twining, Vladimir S. Petrovic, Timothy F. Cootes, William R. Crum, and Christopher J. Taylor

Abstract:

This paper presents a generic method for assessing the quality of non-rigid registration (NRR) algorithms, that does not depend on the existence of any ground truth, but depends solely on the data itself. The data is taken to be a set of images. The output of any non-rigid registration of such a set of images is a dense correspondence across the whole set. Given such a dense correspondence, it is possible to build a generative statistical model of appearance variation across the set. When accurate correspondences are supplied, a good model can be generated. When poor correspondences are given, the model is degraded. By evaluating the quality of the resulting generative model, we obtain a measure of the quality of the correspondences. We derive measures of model specificity and generalisation that can be used to assess the quality of such models, and thus the quality of the original correspondences. It should be noted that this approach does not depend on the specifics of the registration algorithm used or on the specifics of the modelling approach used.

The method is validated by comparing our assessment of registration quality with that derived from overlap measures using ground-truth anatomical labelling. We demonstrate that not only is our approach capable of reliably assessing NRR without ground truth, but it is also more sensitive than the ground-truth-dependent approach. Finally, to demonstrate the practicality of our method, different NRR algorithms - both pairwise and groupwise- are compared in terms of their performance on MR brain data.