Translator Disclaimer
27 March 2009 Bayes estimation of shape model with application to vertebrae boundaries
Author Affiliations +
Proceedings Volume 7259, Medical Imaging 2009: Image Processing; 72590A (2009)
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States
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 Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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 (27 March 2009); doi: 10.1117/12.812434;

Back to Top