Prior knowledge regularization in statistical medical image tasks

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Standard

Prior knowledge regularization in statistical medical image tasks. / Crimi, Alessandro; Sporring, Jon; de Bruijne, Marleen; Lillholm, Martin; Nielsen, Mads.

Proceedings of the MICCAI Workshop on Probabilistic Models for Medical Image Analysis. 2009.

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Harvard

Crimi, A, Sporring, J, de Bruijne, M, Lillholm, M & Nielsen, M 2009, Prior knowledge regularization in statistical medical image tasks. i Proceedings of the MICCAI Workshop on Probabilistic Models for Medical Image Analysis. International Conference on Medical Image Computing and Computer Assisted Intervention, London, Storbritannien, 20/09/2009.

APA

Crimi, A., Sporring, J., de Bruijne, M., Lillholm, M., & Nielsen, M. (2009). Prior knowledge regularization in statistical medical image tasks. I Proceedings of the MICCAI Workshop on Probabilistic Models for Medical Image Analysis

Vancouver

Crimi A, Sporring J, de Bruijne M, Lillholm M, Nielsen M. Prior knowledge regularization in statistical medical image tasks. I Proceedings of the MICCAI Workshop on Probabilistic Models for Medical Image Analysis. 2009

Author

Crimi, Alessandro ; Sporring, Jon ; de Bruijne, Marleen ; Lillholm, Martin ; Nielsen, Mads. / Prior knowledge regularization in statistical medical image tasks. Proceedings of the MICCAI Workshop on Probabilistic Models for Medical Image Analysis. 2009.

Bibtex

@inproceedings{0e516ba0a18a11df928f000ea68e967b,
title = "Prior knowledge regularization in statistical medical image tasks",
abstract = "The estimation of the covariance matrix is a pivotal step inseveral statistical tasks. In particular, the estimation becomes challeng-ing for high dimensional representations of data when few samples areavailable. Using the standard Maximum Likelihood estimation (MLE)when the number of samples are lower than the dimension of the datacan lead to incorrect estimation e.g. of the covariance matrix and subse-quent unreliable results of statistical tasks. This limitation is normallysolved by the well-known Tikhonov regularization adding partially anidentity matrix; here we discuss a Bayesian approach for regularizing thecovariance matrix using prior knowledge. Our method is evaluated forreconstructing and modeling vertebra and cartilage shapes from a lowerdimensional representation and a conditional model. For these centralproblems, the proposed methodology outperforms the traditional MLEmethod and the Tikhonov regularization.",
author = "Alessandro Crimi and Jon Sporring and {de Bruijne}, Marleen and Martin Lillholm and Mads Nielsen",
year = "2009",
language = "English",
booktitle = "Proceedings of the MICCAI Workshop on Probabilistic Models for Medical Image Analysis",

}

RIS

TY - GEN

T1 - Prior knowledge regularization in statistical medical image tasks

AU - Crimi, Alessandro

AU - Sporring, Jon

AU - de Bruijne, Marleen

AU - Lillholm, Martin

AU - Nielsen, Mads

PY - 2009

Y1 - 2009

N2 - The estimation of the covariance matrix is a pivotal step inseveral statistical tasks. In particular, the estimation becomes challeng-ing for high dimensional representations of data when few samples areavailable. Using the standard Maximum Likelihood estimation (MLE)when the number of samples are lower than the dimension of the datacan lead to incorrect estimation e.g. of the covariance matrix and subse-quent unreliable results of statistical tasks. This limitation is normallysolved by the well-known Tikhonov regularization adding partially anidentity matrix; here we discuss a Bayesian approach for regularizing thecovariance matrix using prior knowledge. Our method is evaluated forreconstructing and modeling vertebra and cartilage shapes from a lowerdimensional representation and a conditional model. For these centralproblems, the proposed methodology outperforms the traditional MLEmethod and the Tikhonov regularization.

AB - The estimation of the covariance matrix is a pivotal step inseveral statistical tasks. In particular, the estimation becomes challeng-ing for high dimensional representations of data when few samples areavailable. Using the standard Maximum Likelihood estimation (MLE)when the number of samples are lower than the dimension of the datacan lead to incorrect estimation e.g. of the covariance matrix and subse-quent unreliable results of statistical tasks. This limitation is normallysolved by the well-known Tikhonov regularization adding partially anidentity matrix; here we discuss a Bayesian approach for regularizing thecovariance matrix using prior knowledge. Our method is evaluated forreconstructing and modeling vertebra and cartilage shapes from a lowerdimensional representation and a conditional model. For these centralproblems, the proposed methodology outperforms the traditional MLEmethod and the Tikhonov regularization.

M3 - Article in proceedings

BT - Proceedings of the MICCAI Workshop on Probabilistic Models for Medical Image Analysis

ER -

ID: 21235760