Performance evaluation of parametric bias field correction

V. Zagorodnov; Mads Fogtmann Hansen

doi:10.1109/ICIP.2005.1530496

Performance evaluation of parametric bias field correction

V. Zagorodnov
, Mads Fogtmann Hansen

Research output: Contribution to journal › Conference article › Research › peer-review

Abstract

Magnetic Resonance (MR) images often exhibit grayscale nonuniformities, caused by radio frequency (RF) coil design or acquisition sequences. Many algorithms to remove these nonuniformities have been proposed in the past decade. However, only minor attention has been given to the performance evaluation of existing methods. We derive a link between the estimation performance and underlying image structure. For a piecewise constant 1D signal model with equal size regions we demonstrate that the variance in estimation grows as M2, where M is the number of regions. In 2D case the growth becomes linear in All.

Original language	English
Journal	International Conference on Image Processing. Proceedings
Volume	1-5
Pages (from-to)	3117-3120
ISSN	1522-4880
DOIs	https://doi.org/10.1109/ICIP.2005.1530496
Publication status	Published - 2005
Externally published	Yes
Event	IEEE International Conference on Image Processing 2005 - Genoa, Italy Duration: 11 Sept 2005 → 14 Sept 2005 http://www.informatik.uni-trier.de/~ley/db/conf/icip/icip2005-1.html

Conference

Conference	IEEE International Conference on Image Processing 2005
Country/Territory	Italy
City	Genoa
Period	11/09/2005 → 14/09/2005
Internet address	http://www.informatik.uni-trier.de/~ley/db/conf/icip/icip2005-1.html

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title = "Performance evaluation of parametric bias field correction",

abstract = "Magnetic Resonance (MR) images often exhibit grayscale nonuniformities, caused by radio frequency (RF) coil design or acquisition sequences. Many algorithms to remove these nonuniformities have been proposed in the past decade. However, only minor attention has been given to the performance evaluation of existing methods. We derive a link between the estimation performance and underlying image structure. For a piecewise constant 1D signal model with equal size regions we demonstrate that the variance in estimation grows as M2, where M is the number of regions. In 2D case the growth becomes linear in All.",

author = "V. Zagorodnov and Hansen, \{Mads Fogtmann\}",

year = "2005",

doi = "10.1109/ICIP.2005.1530496",

language = "English",

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pages = "3117--3120",

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AU - Hansen, Mads Fogtmann

PY - 2005

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N2 - Magnetic Resonance (MR) images often exhibit grayscale nonuniformities, caused by radio frequency (RF) coil design or acquisition sequences. Many algorithms to remove these nonuniformities have been proposed in the past decade. However, only minor attention has been given to the performance evaluation of existing methods. We derive a link between the estimation performance and underlying image structure. For a piecewise constant 1D signal model with equal size regions we demonstrate that the variance in estimation grows as M2, where M is the number of regions. In 2D case the growth becomes linear in All.

AB - Magnetic Resonance (MR) images often exhibit grayscale nonuniformities, caused by radio frequency (RF) coil design or acquisition sequences. Many algorithms to remove these nonuniformities have been proposed in the past decade. However, only minor attention has been given to the performance evaluation of existing methods. We derive a link between the estimation performance and underlying image structure. For a piecewise constant 1D signal model with equal size regions we demonstrate that the variance in estimation grows as M2, where M is the number of regions. In 2D case the growth becomes linear in All.

U2 - 10.1109/ICIP.2005.1530496

DO - 10.1109/ICIP.2005.1530496

M3 - Conference article

SN - 1522-4880

VL - 1-5

SP - 3117

EP - 3120

JO - International Conference on Image Processing. Proceedings

JF - International Conference on Image Processing. Proceedings

T2 - IEEE International Conference on Image Processing 2005

Y2 - 11 September 2005 through 14 September 2005

ER -

Performance evaluation of parametric bias field correction

Abstract

Conference

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