21 March 2014 A statistical shape+pose model for segmentation of wrist CT images
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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.
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Emran Mohammad Abu Anas, Abtin Rasoulian, Paul St. John, David Pichora, Robert Rohling, Purang Abolmaesumi, "A statistical shape+pose model for segmentation of wrist CT images", Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90340T (21 March 2014); doi: 10.1117/12.2043092; https://doi.org/10.1117/12.2043092
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