Loading [MathJax]/extensions/MathMenu.js
Maximum a Posteriori Estimation of Linear Shape Variation With Application to Vertebra and Cartilage Modeling | IEEE Journals & Magazine | IEEE Xplore

Maximum a Posteriori Estimation of Linear Shape Variation With Application to Vertebra and Cartilage Modeling


Abstract:

The estimation of covariance matrices is a crucial step in several statistical tasks. Especially when using few samples of a high dimensional representation of shapes, th...Show More

Abstract:

The estimation of covariance matrices is a crucial step in several statistical tasks. Especially when using few samples of a high dimensional representation of shapes, the standard maximum likelihood estimation (ML) of the covariance matrix can be far from the truth, is often rank deficient, and may lead to unreliable results. In this paper, we discuss regularization by prior knowledge using maximum a posteriori (MAP) estimates. We compare ML to MAP using a number of priors and to Tikhonov regularization. We evaluate the covariance estimates on both synthetic and real data, and we analyze the estimates' influence on a missing-data reconstruction task, where high resolution vertebra and cartilage models are reconstructed from incomplete and lower dimensional representations. Our results demonstrate that our methods outperform the traditional ML method and Tikhonov regularization.
Published in: IEEE Transactions on Medical Imaging ( Volume: 30, Issue: 8, August 2011)
Page(s): 1514 - 1526
Date of Publication: 22 March 2011

ISSN Information:

PubMed ID: 21427019

References

References is not available for this document.