@inproceedings{e100536544a34244989957d7732aaf3c,
title = "Initialising groupwise non-rigid registration using multiple parts+geometry models",
abstract = "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.",
author = "Pei Zhang and Yap, {Pew Thian} and Dinggang Shen and Cootes, {Timothy F.}",
year = "2012",
doi = "10.1007/978-3-642-33454-2_20",
language = "English",
isbn = "9783642334535",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "156--163",
editor = "Nicholas Ayache and Herve Delingette and Polina Golland and Kensaku Mori",
booktitle = "Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings",
note = "15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012 ; Conference date: 01-10-2012 Through 05-10-2012",
}