Empirical evaluation of covariance estimates for mutual information coregistration

Paul A. Bromiley; Maja Pokric; Neil A. Thacker

Empirical evaluation of covariance estimates for mutual information coregistration

Paul A. Bromiley, Maja Pokric, Neil A. Thacker

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Mutual information has become a popular similarity measure in multi-modality medical image registration since it was first applied to the problem in 1995. This paper describes a method for calculating the covariance matrix for mutual information coregistration. We derive an expression for the matrix through identification of mutual information with a log-likelihood measure. The validity of this result is then demonstrated through comparison with the results of Monte-Carlo simulations of the coregistration of Tl-weighted to T2-weighted synthetic and genuine MRI scans of the brain. We conclude with some observations on the theoretical basis of the mutual information measure as a log-likelihood. © Springer-Verlag Berlin Heidelberg 2004.

Original language	English
Title of host publication	Lecture Notes in Computer Science\|Lect. Notes Comput. Sci.
Editors	C. Barillot, D.R. Haynor, P. Hellier
Place of Publication	Berlin Heidelberg
Publisher	Springer Nature
Pages	607-614
Number of pages	7
Volume	3216
Publication status	Published - 2004
Event	Medical Image Computing and Computer-Assisted Intervention, MICCAI 2004 - 7th International Conference, Proceedings - Saint-Malo Duration: 1 Jul 2004 → …

Conference

Conference	Medical Image Computing and Computer-Assisted Intervention, MICCAI 2004 - 7th International Conference, Proceedings
City	Saint-Malo
Period	1/07/04 → …

Cite this

@inproceedings{c2810967a1a14954b766432240a77176,

title = "Empirical evaluation of covariance estimates for mutual information coregistration",

abstract = "Mutual information has become a popular similarity measure in multi-modality medical image registration since it was first applied to the problem in 1995. This paper describes a method for calculating the covariance matrix for mutual information coregistration. We derive an expression for the matrix through identification of mutual information with a log-likelihood measure. The validity of this result is then demonstrated through comparison with the results of Monte-Carlo simulations of the coregistration of Tl-weighted to T2-weighted synthetic and genuine MRI scans of the brain. We conclude with some observations on the theoretical basis of the mutual information measure as a log-likelihood. {\textcopyright} Springer-Verlag Berlin Heidelberg 2004.",

author = "Bromiley, {Paul A.} and Maja Pokric and Thacker, {Neil A.}",

year = "2004",

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volume = "3216",

pages = "607--614",

editor = "C. Barillot and D.R. Haynor and P. Hellier",

booktitle = "Lecture Notes in Computer Science|Lect. Notes Comput. Sci.",

publisher = "Springer Nature",

address = "United States",

note = "Medical Image Computing and Computer-Assisted Intervention, MICCAI 2004 - 7th International Conference, Proceedings ; Conference date: 01-07-2004",

}

Bromiley, PA, Pokric, M & Thacker, NA 2004, Empirical evaluation of covariance estimates for mutual information coregistration. in C Barillot, DR Haynor & P Hellier (eds), Lecture Notes in Computer Science|Lect. Notes Comput. Sci.. vol. 3216, Springer Nature, Berlin Heidelberg, pp. 607-614, Medical Image Computing and Computer-Assisted Intervention, MICCAI 2004 - 7th International Conference, Proceedings, Saint-Malo, 1/07/04.

Empirical evaluation of covariance estimates for mutual information coregistration. / Bromiley, Paul A.; Pokric, Maja; Thacker, Neil A.
Lecture Notes in Computer Science|Lect. Notes Comput. Sci.. ed. / C. Barillot; D.R. Haynor; P. Hellier. Vol. 3216 Berlin Heidelberg: Springer Nature, 2004. p. 607-614.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Empirical evaluation of covariance estimates for mutual information coregistration

AU - Bromiley, Paul A.

AU - Pokric, Maja

AU - Thacker, Neil A.

PY - 2004

Y1 - 2004

N2 - Mutual information has become a popular similarity measure in multi-modality medical image registration since it was first applied to the problem in 1995. This paper describes a method for calculating the covariance matrix for mutual information coregistration. We derive an expression for the matrix through identification of mutual information with a log-likelihood measure. The validity of this result is then demonstrated through comparison with the results of Monte-Carlo simulations of the coregistration of Tl-weighted to T2-weighted synthetic and genuine MRI scans of the brain. We conclude with some observations on the theoretical basis of the mutual information measure as a log-likelihood. © Springer-Verlag Berlin Heidelberg 2004.

AB - Mutual information has become a popular similarity measure in multi-modality medical image registration since it was first applied to the problem in 1995. This paper describes a method for calculating the covariance matrix for mutual information coregistration. We derive an expression for the matrix through identification of mutual information with a log-likelihood measure. The validity of this result is then demonstrated through comparison with the results of Monte-Carlo simulations of the coregistration of Tl-weighted to T2-weighted synthetic and genuine MRI scans of the brain. We conclude with some observations on the theoretical basis of the mutual information measure as a log-likelihood. © Springer-Verlag Berlin Heidelberg 2004.

M3 - Conference contribution

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SP - 607

EP - 614

BT - Lecture Notes in Computer Science|Lect. Notes Comput. Sci.

A2 - Barillot, C.

A2 - Haynor, D.R.

A2 - Hellier, P.

PB - Springer Nature

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ER -

Empirical evaluation of covariance estimates for mutual information coregistration

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