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Proceedings Paper

Bayes estimation of shape model with application to vertebrae boundaries
Author(s): Alessandro Crimi; Anarta Ghosh; Jon Sporring; Mads Nielsen
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Paper Abstract

Estimation of the covariance matrix is a pivotal step in landmark based statistical shape analysis. For high dimensional representation of the shapes, often the number of available shape examples is far too small for ML covariance matrix is rank deficient and eigenvectors corresponding to the small eigenvalues. We take a Bayesian approach to the problem and show how the prior information can be used to estimate the covariance matrix from a small number of samples in a high dimensional shape space. The performance of the proposed method is evaluated in the context of reconstructions of high resolution vertebral boundary from an incomplete and lower dimensional representation. The algorithm performs better than the ML method, especially for small numbers of samples in the training set. The superiority of the proposed Bayesian approach was also observed when noisy incomplete lower dimensional representation of the vertebral boundary was used in the reconstruction algorithm. Moreover, unlike other commonly used approaches, e.g., regularization, the presented method does not depend heavily on the choice of the parameter values.

Paper Details

Date Published: 27 March 2009
PDF: 8 pages
Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72590A (27 March 2009); doi: 10.1117/12.812434
Show Author Affiliations
Alessandro Crimi, Univ. of Copenhagen (Denmark)
Anarta Ghosh, Nordic Bioscience A/S (Denmark)
Jon Sporring, Univ. of Copenhagen (Denmark)
Mads Nielsen, Univ. of Copenhagen (Denmark)
Nordic Bioscience A/S (Denmark)

Published in SPIE Proceedings Vol. 7259:
Medical Imaging 2009: Image Processing
Josien P. W. Pluim; Benoit M. Dawant, Editor(s)

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