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Improving the Performance of the Prony Method Using a Wavelet Domain Filter for MRI Denoising

The Prony methods are used for exponential fitting. We use a variant of the Prony method for abnormal brain tissue detection in sequences of T (2) weighted magnetic resonance images. Here, MR images are considered to be affected only by Rician noise, and a new wavelet domain bilateral filtering proc...

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Detalles Bibliográficos
Autores principales: Jaramillo, Rodney, Lentini, Marianela, Paluszny, Marco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4009158/
https://www.ncbi.nlm.nih.gov/pubmed/24834108
http://dx.doi.org/10.1155/2014/810680
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author Jaramillo, Rodney
Lentini, Marianela
Paluszny, Marco
author_facet Jaramillo, Rodney
Lentini, Marianela
Paluszny, Marco
author_sort Jaramillo, Rodney
collection PubMed
description The Prony methods are used for exponential fitting. We use a variant of the Prony method for abnormal brain tissue detection in sequences of T (2) weighted magnetic resonance images. Here, MR images are considered to be affected only by Rician noise, and a new wavelet domain bilateral filtering process is implemented to reduce the noise in the images. This filter is a modification of Kazubek's algorithm and we use synthetic images to show the ability of the new procedure to suppress noise and compare its performance with respect to the original filter, using quantitative and qualitative criteria. The tissue classification process is illustrated using a real sequence of T (2) MR images, and the filter is applied to each image before using the variant of the Prony method.
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spelling pubmed-40091582014-05-15 Improving the Performance of the Prony Method Using a Wavelet Domain Filter for MRI Denoising Jaramillo, Rodney Lentini, Marianela Paluszny, Marco Comput Math Methods Med Research Article The Prony methods are used for exponential fitting. We use a variant of the Prony method for abnormal brain tissue detection in sequences of T (2) weighted magnetic resonance images. Here, MR images are considered to be affected only by Rician noise, and a new wavelet domain bilateral filtering process is implemented to reduce the noise in the images. This filter is a modification of Kazubek's algorithm and we use synthetic images to show the ability of the new procedure to suppress noise and compare its performance with respect to the original filter, using quantitative and qualitative criteria. The tissue classification process is illustrated using a real sequence of T (2) MR images, and the filter is applied to each image before using the variant of the Prony method. Hindawi Publishing Corporation 2014 2014-04-14 /pmc/articles/PMC4009158/ /pubmed/24834108 http://dx.doi.org/10.1155/2014/810680 Text en Copyright © 2014 Rodney Jaramillo et al. 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 Research Article
Jaramillo, Rodney
Lentini, Marianela
Paluszny, Marco
Improving the Performance of the Prony Method Using a Wavelet Domain Filter for MRI Denoising
title Improving the Performance of the Prony Method Using a Wavelet Domain Filter for MRI Denoising
title_full Improving the Performance of the Prony Method Using a Wavelet Domain Filter for MRI Denoising
title_fullStr Improving the Performance of the Prony Method Using a Wavelet Domain Filter for MRI Denoising
title_full_unstemmed Improving the Performance of the Prony Method Using a Wavelet Domain Filter for MRI Denoising
title_short Improving the Performance of the Prony Method Using a Wavelet Domain Filter for MRI Denoising
title_sort improving the performance of the prony method using a wavelet domain filter for mri denoising
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4009158/
https://www.ncbi.nlm.nih.gov/pubmed/24834108
http://dx.doi.org/10.1155/2014/810680
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