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A Review on MR Image Intensity Inhomogeneity Correction

Intensity inhomogeneity (IIH) is often encountered in MR imaging, and a number of techniques have been devised to correct this artifact. This paper attempts to review some of the recent developments in the mathematical modeling of IIH field. Low-frequency models are widely used, but they tend to cor...

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Detalles Bibliográficos
Autor principal: Hou, Zujun
Formato: Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2006
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2324029/
https://www.ncbi.nlm.nih.gov/pubmed/23165035
http://dx.doi.org/10.1155/IJBI/2006/49515
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author Hou, Zujun
author_facet Hou, Zujun
author_sort Hou, Zujun
collection PubMed
description Intensity inhomogeneity (IIH) is often encountered in MR imaging, and a number of techniques have been devised to correct this artifact. This paper attempts to review some of the recent developments in the mathematical modeling of IIH field. Low-frequency models are widely used, but they tend to corrupt the low-frequency components of the tissue. Hypersurface models and statistical models can be adaptive to the image and generally more stable, but they are also generally more complex and consume more computer memory and CPU time. They are often formulated together with image segmentation within one framework and the overall performance is highly dependent on the segmentation process. Beside these three popular models, this paper also summarizes other techniques based on different principles. In addition, the issue of quantitative evaluation and comparative study are discussed.
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spelling pubmed-23240292008-04-22 A Review on MR Image Intensity Inhomogeneity Correction Hou, Zujun Int J Biomed Imaging Article Intensity inhomogeneity (IIH) is often encountered in MR imaging, and a number of techniques have been devised to correct this artifact. This paper attempts to review some of the recent developments in the mathematical modeling of IIH field. Low-frequency models are widely used, but they tend to corrupt the low-frequency components of the tissue. Hypersurface models and statistical models can be adaptive to the image and generally more stable, but they are also generally more complex and consume more computer memory and CPU time. They are often formulated together with image segmentation within one framework and the overall performance is highly dependent on the segmentation process. Beside these three popular models, this paper also summarizes other techniques based on different principles. In addition, the issue of quantitative evaluation and comparative study are discussed. Hindawi Publishing Corporation 2006 2006-08-07 /pmc/articles/PMC2324029/ /pubmed/23165035 http://dx.doi.org/10.1155/IJBI/2006/49515 Text en Copyright © 2006 Z. Hou https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Article
Hou, Zujun
A Review on MR Image Intensity Inhomogeneity Correction
title A Review on MR Image Intensity Inhomogeneity Correction
title_full A Review on MR Image Intensity Inhomogeneity Correction
title_fullStr A Review on MR Image Intensity Inhomogeneity Correction
title_full_unstemmed A Review on MR Image Intensity Inhomogeneity Correction
title_short A Review on MR Image Intensity Inhomogeneity Correction
title_sort review on mr image intensity inhomogeneity correction
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2324029/
https://www.ncbi.nlm.nih.gov/pubmed/23165035
http://dx.doi.org/10.1155/IJBI/2006/49515
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