Groupwise non-rigid registration is an important technique in medical image analysis. Recent studies show that its accuracy can be greatly improved by explicitly providing good initialisation. This is achieved by seeking a sparse correspondence using a parts+geometry model. In this paper we show that a single parts+geometry model is unlikely to establish consistent sparse correspondence for complex objects, and that better initialisation can be achieved using a set of models. We describe how to combine the strengths of multiple models, and demonstrate that the method gives state-of-the-art performance on three datasets, with the most significant improvement on the most challenging.
|Title of host publication||Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings|
|Editors||Nicholas Ayache, Herve Delingette, Polina Golland, Kensaku Mori|
|Number of pages||8|
|Publication status||Published - 2012|
|Event||15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012 - Nice, France|
Duration: 2012 Oct 1 → 2012 Oct 5
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012|
|Period||12/10/1 → 12/10/5|
Bibliographical notePublisher Copyright:
© Springer-Verlag Berlin Heidelberg 2012.
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)