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Proceedings Article

Bayes estimation of shape model with application to vertebrae boundaries

[+] Author Affiliations
Alessandro Crimi, Jon Sporring

Univ. of Copenhagen (Denmark)

Anarta Ghosh

Nordic Bioscience A/S (Denmark)

Mads Nielsen

Univ. of Copenhagen (Denmark) and Nordic Bioscience A/S (Denmark)

Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72590A (March 27, 2009); doi:10.1117/12.812434
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From Conference Volume 7259

  • Medical Imaging 2009: Image Processing
  • Josien P. W. Pluim; Benoit M. Dawant
  • Lake Buena Vista, FL | February 07, 2009

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.

© (2009) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Citation

Alessandro Crimi ; Anarta Ghosh ; Jon Sporring and Mads Nielsen
"Bayes estimation of shape model with application to vertebrae boundaries", Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72590A (March 27, 2009); doi:10.1117/12.812434; http://dx.doi.org/10.1117/12.812434


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