Correlation modeling for compression of computed tomography images

Abstract-Computed Tomography (CT) is a noninvasive medical test obtained via a series of X-ray exposures resulting in 3D images that aid medical diagnosis. Previous approaches for coding such 3D images propose to employ multi-component transforms to exploit correlation among CT slices, but these app...

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Autores: Muñoz Gómez, Juan, Bartrina-Rapesta, Joan|||0000-0002-1551-3680, Marcellin, Michael W.|||0000-0001-9606-134X, Serra-Sagristà, Joan|||0000-0003-4729-9292
Formato: artículo
Fecha de publicación:2013
País:España
Recursos:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:129770
Acesso em linha:https://ddd.uab.cat/record/129770
https://dx.doi.org/urn:doi:10.1109/JBHI.2013.2264595
Access Level:acceso abierto
Palavra-chave:Computed tomography image compression
Correlation modeling
Multi-component transforms
JPEG2000 coding standard
DICOM protocol
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spelling Correlation modeling for compression of computed tomography imagesMuñoz Gómez, JuanBartrina-Rapesta, Joan|||0000-0002-1551-3680Marcellin, Michael W.|||0000-0001-9606-134XSerra-Sagristà, Joan|||0000-0003-4729-9292Computed tomography image compressionCorrelation modelingMulti-component transformsJPEG2000 coding standardDICOM protocolAbstract-Computed Tomography (CT) is a noninvasive medical test obtained via a series of X-ray exposures resulting in 3D images that aid medical diagnosis. Previous approaches for coding such 3D images propose to employ multi-component transforms to exploit correlation among CT slices, but these approaches do not always improve coding performance with respect to a simpler slice-by-slice coding approach. In this work, we propose a novel analysis which accurately predicts when the use of a multi-component transform is profitable. This analysis models the correlation coefficient r based on image acquisition parameters readily available at acquisition time. Extensive experimental results from multiple image sensors suggest that multi-component transforms are appropriate for images with correlation coefficient r in excess of 0.87. 22013-01-0120132013-01-01Articlehttp://purl.org/coar/resource_type/c_6501SMURhttp://purl.org/coar/version/c_71e4c1898caa6e32info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/129770https://dx.doi.org/urn:doi:10.1109/JBHI.2013.2264595reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengEuropean Commission https://doi.org/10.13039/501100000780 250420Ministerio de Ciencia e Innovación https://doi.org/10.13039/501100004837 TIN-2009-14426-C02-01Ministerio de Economía y Competitividad https://doi.org/10.13039/501100003329 TIN-2012-38102-C03-03Agència de Gestió d'Ajuts Universitaris i de Recerca https://doi.org/10.13039/501100003030 2009/SGR-1224open accesshttp://purl.org/coar/access_right/c_abf2Aquest material està protegit per drets d'autor i/o drets afins. Podeu utilitzar aquest material en funció del que permet la legislació de drets d'autor i drets afins d'aplicació al vostre cas. Per a d'altres usos heu d'obtenir permís del(s) titular(s) de drets.https://rightsstatements.org/vocab/InC/1.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:1297702026-06-06T12:50:31Z
dc.title.none.fl_str_mv Correlation modeling for compression of computed tomography images
title Correlation modeling for compression of computed tomography images
spellingShingle Correlation modeling for compression of computed tomography images
Muñoz Gómez, Juan
Computed tomography image compression
Correlation modeling
Multi-component transforms
JPEG2000 coding standard
DICOM protocol
title_short Correlation modeling for compression of computed tomography images
title_full Correlation modeling for compression of computed tomography images
title_fullStr Correlation modeling for compression of computed tomography images
title_full_unstemmed Correlation modeling for compression of computed tomography images
title_sort Correlation modeling for compression of computed tomography images
dc.creator.none.fl_str_mv Muñoz Gómez, Juan
Bartrina-Rapesta, Joan|||0000-0002-1551-3680
Marcellin, Michael W.|||0000-0001-9606-134X
Serra-Sagristà, Joan|||0000-0003-4729-9292
author Muñoz Gómez, Juan
author_facet Muñoz Gómez, Juan
Bartrina-Rapesta, Joan|||0000-0002-1551-3680
Marcellin, Michael W.|||0000-0001-9606-134X
Serra-Sagristà, Joan|||0000-0003-4729-9292
author_role author
author2 Bartrina-Rapesta, Joan|||0000-0002-1551-3680
Marcellin, Michael W.|||0000-0001-9606-134X
Serra-Sagristà, Joan|||0000-0003-4729-9292
author2_role author
author
author
dc.subject.none.fl_str_mv Computed tomography image compression
Correlation modeling
Multi-component transforms
JPEG2000 coding standard
DICOM protocol
topic Computed tomography image compression
Correlation modeling
Multi-component transforms
JPEG2000 coding standard
DICOM protocol
description Abstract-Computed Tomography (CT) is a noninvasive medical test obtained via a series of X-ray exposures resulting in 3D images that aid medical diagnosis. Previous approaches for coding such 3D images propose to employ multi-component transforms to exploit correlation among CT slices, but these approaches do not always improve coding performance with respect to a simpler slice-by-slice coding approach. In this work, we propose a novel analysis which accurately predicts when the use of a multi-component transform is profitable. This analysis models the correlation coefficient r based on image acquisition parameters readily available at acquisition time. Extensive experimental results from multiple image sensors suggest that multi-component transforms are appropriate for images with correlation coefficient r in excess of 0.87.
publishDate 2013
dc.date.none.fl_str_mv 2
2013-01-01
2013
2013-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
SMUR
http://purl.org/coar/version/c_71e4c1898caa6e32
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/129770
https://dx.doi.org/urn:doi:10.1109/JBHI.2013.2264595
url https://ddd.uab.cat/record/129770
https://dx.doi.org/urn:doi:10.1109/JBHI.2013.2264595
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission https://doi.org/10.13039/501100000780 250420
Ministerio de Ciencia e Innovación https://doi.org/10.13039/501100004837 TIN-2009-14426-C02-01
Ministerio de Economía y Competitividad https://doi.org/10.13039/501100003329 TIN-2012-38102-C03-03
Agència de Gestió d'Ajuts Universitaris i de Recerca https://doi.org/10.13039/501100003030 2009/SGR-1224
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://rightsstatements.org/vocab/InC/1.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://rightsstatements.org/vocab/InC/1.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
collection Dipòsit Digital de Documents de la UAB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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