Ravikumar, N., Gooya, A., Cimen, S. et al. (2 more authors) (2016) A Multi-Resolution t-Mixture Model Approach to Robust Group-wise Alignment of Shapes. In: Lecture Notes in Computer Science. Medical Image Computing and Computer Assisted Interventions (MICCAI), 17-21 Oct 2016, Athens, Greece. Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2016 . , pp. 142-149. ISBN 978-3-319-46725-2
Abstract
A novel probabilistic, group-wise rigid registration framework is proposed in this study, to robustly align and establish correspondence across anatomical shapes represented as unstructured point sets. Student’s t-mixture model (TMM) is employed to exploit their inherent robustness to outliers. The primary application for such a framework is the automatic construction of statistical shape models (SSMs) of anatomical structures, from medical images. Tools used for automatic segmentation and landmarking of medical images often result in segmentations with varying proportions of outliers. The proposed approach is able to robustly align shapes and establish valid correspondences in the presence of considerable outliers and large variations in shape. A multi-resolution registration (mrTMM) framework is also formulated, to further improve the performance of the proposed TMM-based registration method. Comparisons with a state-of-the art approach using clinical data show that the mrTMM method in particular, achieves higher alignment accuracy and yields SSMs that generalise better to unseen shapes.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 Springer Verlag. This is an author produced version of a paper subsequently published in Lecture Notes in Computer Science. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Mechanical Engineering (Sheffield) |
Funding Information: | Funder Grant number EUROPEAN COMMISSION - FP6/FP7 VPH DARE - 601055 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 30 Nov 2016 15:43 |
Last Modified: | 19 Dec 2022 13:35 |
Published Version: | http://doi.org/10.1007/978-3-319-46726-9_17 |
Status: | Published |
Series Name: | Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2016 |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-319-46726-9_17 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:108734 |