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Proceedings Paper

A statistical shape+pose model for segmentation of wrist CT images
Author(s): Emran Mohammad Abu Anas; Abtin Rasoulian; Paul St. John; David Pichora; Robert Rohling; Purang Abolmaesumi
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Paper Abstract

In recent years, there has been significant interest to develop a model of the wrist joint that can capture the statistics of shape and pose variations in a patient population. Such a model could have several clinical applications such as bone segmentation, kinematic analysis and prosthesis development. In this paper, we present a novel statistical model of the wrist joint based on the analysis of shape and pose variations of carpal bones across a group of subjects. The carpal bones are jointly aligned using a group-wise Gaussian Mixture Model registration technique, where principal component analysis is used to determine the mean shape and the main modes of its variations. The pose statistics are determined by using principal geodesics analysis, where statistics of similarity transformations between individual subjects and the mean shape are computed in a linear tangent space. We also demonstrate an application of the model for segmentation of wrist CT images.

Paper Details

Date Published: 21 March 2014
PDF: 8 pages
Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90340T (21 March 2014); doi: 10.1117/12.2043092
Show Author Affiliations
Emran Mohammad Abu Anas, Univ. of British Columbia (Canada)
Abtin Rasoulian, Univ. of British Columbia (Canada)
Paul St. John, Kingston General Hospital (Canada)
David Pichora, Kingston General Hospital (Canada)
Robert Rohling, Univ. of British Columbia (Canada)
Purang Abolmaesumi, Univ. of British Columbia (Canada)

Published in SPIE Proceedings Vol. 9034:
Medical Imaging 2014: Image Processing
Sebastien Ourselin; Martin A. Styner, Editor(s)

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