A multi-resolution T-mixture model approach to robust group-wise alignment of shapes

Ravikumar, N., Gooya, A. , Cimen, S., Frangi, A.F. and Taylor, Z.A. (2016) A multi-resolution T-mixture model approach to robust group-wise alignment of shapes. In: Ourselin, S., Joskowicz, L., Sabuncu, M. R., Unal, G. and Wells, W. (eds.) Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016. Series: Lecture notes in computer science (9902). Springer, pp. 142-149. ISBN 9783319467252 (doi: 10.1007/978-3-319-46726-9_17)

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.

• Item Type: Book Sections
• Additional Information: eISBN: 9783319467269.
• Status: Published
• Glasgow Author(s) Enlighten ID: Gooya, Dr Ali
• Authors: Ravikumar, N., Gooya, A., Cimen, S., Frangi, A.F., and Taylor, Z.A.
• College/School: College of Science and Engineering > School of Computing Science
• Publisher: Springer
• ISBN: 9783319467252

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