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Proceedings Paper

Robust group-wise rigid registration of point sets using t-mixture model
Author(s): Nishant Ravikumar; Ali Gooya; Alejandro F. Frangi; Zeike A. Taylor
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Paper Abstract

A probabilistic framework for robust, group-wise rigid alignment of point-sets using a mixture of Students t-distribution especially when the point sets are of varying lengths, are corrupted by an unknown degree of outliers or in the presence of missing data. Medical images (in particular magnetic resonance (MR) images), their segmentations and consequently point-sets generated from these are highly susceptible to corruption by outliers. This poses a problem for robust correspondence estimation and accurate alignment of shapes, necessary for training statistical shape models (SSMs). To address these issues, this study proposes to use a t-mixture model (TMM), to approximate the underlying joint probability density of a group of similar shapes and align them to a common reference frame. The heavy-tailed nature of t-distributions provides a more robust registration framework in comparison to state of the art algorithms. Significant reduction in alignment errors is achieved in the presence of outliers, using the proposed TMM-based group-wise rigid registration method, in comparison to its Gaussian mixture model (GMM) counterparts. The proposed TMM-framework is compared with a group-wise variant of the well-known Coherent Point Drift (CPD) algorithm and two other group-wise methods using GMMs, using both synthetic and real data sets. Rigid alignment errors for groups of shapes are quantified using the Hausdorff distance (HD) and quadratic surface distance (QSD) metrics.

Paper Details

Date Published: 21 March 2016
PDF: 14 pages
Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97840S (21 March 2016); doi: 10.1117/12.2216244
Show Author Affiliations
Nishant Ravikumar, The Univ. of Sheffield (United Kingdom)
Ali Gooya, The Univ. of Sheffield (United Kingdom)
Alejandro F. Frangi, The Univ. of Sheffield (United Kingdom)
Zeike A. Taylor, The Univ. of Sheffield (United Kingdom)

Published in SPIE Proceedings Vol. 9784:
Medical Imaging 2016: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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