Automatic learning sparse correspondences for initialising groupwise registration

Authors:
Pei Zhang;Steve A. Adeshina;Timothy F. Cootes
Affiliations:
Imaging Science and Biomedical Engineering, The University of Manchester, UK;Imaging Science and Biomedical Engineering, The University of Manchester, UK;Imaging Science and Biomedical Engineering, The University of Manchester, UK
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Year:
2010

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Abstract

We seek to automatically establish dense correspondences across groups of images. Existing non-rigid registration methods usually involve local optimisation and thus require accurate initialisation. It is difficult to obtain such initialisation for images of complex structures, especially those with many self-similar parts. In this paper we show that satisfactory initialisation for such images can be found by a parts+geometry model. We use a population based optimisation strategy to select the best parts from a large pool of candidates. The best matches of the optimal model are used to initialise a groupwise registration algorithm, leading to dense, accurate results. We demonstrate the efficacy of the approach on two challenging datasets, and report on a detailed quantitative evaluation of its performance.