Maximum a posteriori estimation of linear shape variation with application to vertebra and cartilage modeling

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Maximum a posteriori estimation of linear shape variation with application to vertebra and cartilage modeling. / Crimi, Alessandro; Lillholm, Martin; Nielsen, Mads; Ghosh, Anarta; de Bruijne, Marleen; Dam, Erik B.; Sporring, Jon.

I: IEEE transactions on medical imaging, Bind 30, Nr. 8, 2011, s. 1514-1526.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Crimi, A, Lillholm, M, Nielsen, M, Ghosh, A, de Bruijne, M, Dam, EB & Sporring, J 2011, 'Maximum a posteriori estimation of linear shape variation with application to vertebra and cartilage modeling', IEEE transactions on medical imaging, bind 30, nr. 8, s. 1514-1526. https://doi.org/10.1109/TMI.2011.2131150

APA

Crimi, A., Lillholm, M., Nielsen, M., Ghosh, A., de Bruijne, M., Dam, E. B., & Sporring, J. (2011). Maximum a posteriori estimation of linear shape variation with application to vertebra and cartilage modeling. IEEE transactions on medical imaging, 30(8), 1514-1526. https://doi.org/10.1109/TMI.2011.2131150

Vancouver

Crimi A, Lillholm M, Nielsen M, Ghosh A, de Bruijne M, Dam EB o.a. Maximum a posteriori estimation of linear shape variation with application to vertebra and cartilage modeling. IEEE transactions on medical imaging. 2011;30(8):1514-1526. https://doi.org/10.1109/TMI.2011.2131150

Author

Crimi, Alessandro ; Lillholm, Martin ; Nielsen, Mads ; Ghosh, Anarta ; de Bruijne, Marleen ; Dam, Erik B. ; Sporring, Jon. / Maximum a posteriori estimation of linear shape variation with application to vertebra and cartilage modeling. I: IEEE transactions on medical imaging. 2011 ; Bind 30, Nr. 8. s. 1514-1526.

Bibtex

@article{3110034179c64183be24c85d6f3a739c,
title = "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, 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.",
author = "Alessandro Crimi and Martin Lillholm and Mads Nielsen and Anarta Ghosh and {de Bruijne}, Marleen and Dam, {Erik B.} and Jon Sporring",
year = "2011",
doi = "10.1109/TMI.2011.2131150",
language = "English",
volume = "30",
pages = "1514--1526",
journal = "I E E E Transactions on Medical Imaging",
issn = "0278-0062",
publisher = "Institute of Electrical and Electronics Engineers",
number = "8",

}

RIS

TY - JOUR

T1 - Maximum a posteriori estimation of linear shape variation with application to vertebra and cartilage modeling

AU - Crimi, Alessandro

AU - Lillholm, Martin

AU - Nielsen, Mads

AU - Ghosh, Anarta

AU - de Bruijne, Marleen

AU - Dam, Erik B.

AU - Sporring, Jon

PY - 2011

Y1 - 2011

N2 - 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.

AB - 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.

U2 - 10.1109/TMI.2011.2131150

DO - 10.1109/TMI.2011.2131150

M3 - Journal article

C2 - 21427019

VL - 30

SP - 1514

EP - 1526

JO - I E E E Transactions on Medical Imaging

JF - I E E E Transactions on Medical Imaging

SN - 0278-0062

IS - 8

ER -

ID: 33949906