Saliency-based characterization of group differences for magnetic resonance disease classification

Anatomical variability of patient's brains limits the statistical analyses about presence or absence of a pathology. In this paper, we present an approach for classification of brain Magnetic Resonance (MR) images from healthy and diseased subjects. The approach builds up a saliency map, which...

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Autores:
Rueda Olarte, Andrea del Pilar
González Osorio, Fabio Augusto
Romero Castro, Eduardo
Tipo de recurso:
Article of journal
Fecha de publicación:
2013
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/39493
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/39493
http://bdigital.unal.edu.co/29590/
Palabra clave:
Subject classification
Magnetic Resonance Imaging
Visual Attention models
Saliency maps
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
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spelling Atribución-NoComercial 4.0 InternacionalDerechos reservados - Universidad Nacional de Colombiahttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Rueda Olarte, Andrea del Pilar1fe3211d-68d3-4d1e-aab0-bcf944d8a958300González Osorio, Fabio Augustocb310629-a242-42c5-8301-883fbab8e3ab300Romero Castro, Eduardo1cd0e643-6618-4a24-afd7-47d003c026693002019-06-28T03:57:42Z2019-06-28T03:57:42Z2013https://repositorio.unal.edu.co/handle/unal/39493http://bdigital.unal.edu.co/29590/Anatomical variability of patient's brains limits the statistical analyses about presence or absence of a pathology. In this paper, we present an approach for classification of brain Magnetic Resonance (MR) images from healthy and diseased subjects. The approach builds up a saliency map, which extract regions of relative change in three different dimensions: intensity, orientation and edges. The obtained regions of interest are used as suitable patterns for subject classification using support vector machines. The strategy’s performance was assessed on a set of 198 MR images extracted from the OASIS database and divided into four groups, reporting an average accuracy rate of 74.54% and an average Equal Error Rate of 0.725.application/pdfspaUniversidad Nacional de Colombia Sede Medellínhttp://revistas.unal.edu.co/index.php/dyna/article/view/28122Universidad Nacional de Colombia Revistas electrónicas UN DynaDynaDyna; Vol. 80, núm. 178 (2013); 21-28 DYNA; Vol. 80, núm. 178 (2013); 21-28 2346-2183 0012-7353Rueda Olarte, Andrea del Pilar and González Osorio, Fabio Augusto and Romero Castro, Eduardo (2013) Saliency-based characterization of group differences for magnetic resonance disease classification. Dyna; Vol. 80, núm. 178 (2013); 21-28 DYNA; Vol. 80, núm. 178 (2013); 21-28 2346-2183 0012-7353 .Saliency-based characterization of group differences for magnetic resonance disease classificationArtículo de revistainfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/ARTSubject classificationMagnetic Resonance ImagingVisual Attention modelsSaliency mapsORIGINAL28122-197459-1-PB.htmltext/html36943https://repositorio.unal.edu.co/bitstream/unal/39493/1/28122-197459-1-PB.html19d767c8112cf57c97b9853ae5f69384MD5128122-167417-1-PB.pdfapplication/pdf906050https://repositorio.unal.edu.co/bitstream/unal/39493/2/28122-167417-1-PB.pdf3aaf5f910f728a2f7ea14dedf6fceff0MD52THUMBNAIL28122-167417-1-PB.pdf.jpg28122-167417-1-PB.pdf.jpgGenerated Thumbnailimage/jpeg9416https://repositorio.unal.edu.co/bitstream/unal/39493/3/28122-167417-1-PB.pdf.jpg7d5ce5ac65ed895866f18b3698c08e3bMD53unal/39493oai:repositorio.unal.edu.co:unal/394932023-01-24 23:03:27.208Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.co
dc.title.spa.fl_str_mv Saliency-based characterization of group differences for magnetic resonance disease classification
title Saliency-based characterization of group differences for magnetic resonance disease classification
spellingShingle Saliency-based characterization of group differences for magnetic resonance disease classification
Subject classification
Magnetic Resonance Imaging
Visual Attention models
Saliency maps
title_short Saliency-based characterization of group differences for magnetic resonance disease classification
title_full Saliency-based characterization of group differences for magnetic resonance disease classification
title_fullStr Saliency-based characterization of group differences for magnetic resonance disease classification
title_full_unstemmed Saliency-based characterization of group differences for magnetic resonance disease classification
title_sort Saliency-based characterization of group differences for magnetic resonance disease classification
dc.creator.fl_str_mv Rueda Olarte, Andrea del Pilar
González Osorio, Fabio Augusto
Romero Castro, Eduardo
dc.contributor.author.spa.fl_str_mv Rueda Olarte, Andrea del Pilar
González Osorio, Fabio Augusto
Romero Castro, Eduardo
dc.subject.proposal.spa.fl_str_mv Subject classification
Magnetic Resonance Imaging
Visual Attention models
Saliency maps
topic Subject classification
Magnetic Resonance Imaging
Visual Attention models
Saliency maps
description Anatomical variability of patient's brains limits the statistical analyses about presence or absence of a pathology. In this paper, we present an approach for classification of brain Magnetic Resonance (MR) images from healthy and diseased subjects. The approach builds up a saliency map, which extract regions of relative change in three different dimensions: intensity, orientation and edges. The obtained regions of interest are used as suitable patterns for subject classification using support vector machines. The strategy’s performance was assessed on a set of 198 MR images extracted from the OASIS database and divided into four groups, reporting an average accuracy rate of 74.54% and an average Equal Error Rate of 0.725.
publishDate 2013
dc.date.issued.spa.fl_str_mv 2013
dc.date.accessioned.spa.fl_str_mv 2019-06-28T03:57:42Z
dc.date.available.spa.fl_str_mv 2019-06-28T03:57:42Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.eprints.spa.fl_str_mv http://bdigital.unal.edu.co/29590/
url https://repositorio.unal.edu.co/handle/unal/39493
http://bdigital.unal.edu.co/29590/
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.spa.fl_str_mv http://revistas.unal.edu.co/index.php/dyna/article/view/28122
dc.relation.ispartof.spa.fl_str_mv Universidad Nacional de Colombia Revistas electrónicas UN Dyna
Dyna
dc.relation.ispartofseries.none.fl_str_mv Dyna; Vol. 80, núm. 178 (2013); 21-28 DYNA; Vol. 80, núm. 178 (2013); 21-28 2346-2183 0012-7353
dc.relation.references.spa.fl_str_mv Rueda Olarte, Andrea del Pilar and González Osorio, Fabio Augusto and Romero Castro, Eduardo (2013) Saliency-based characterization of group differences for magnetic resonance disease classification. Dyna; Vol. 80, núm. 178 (2013); 21-28 DYNA; Vol. 80, núm. 178 (2013); 21-28 2346-2183 0012-7353 .
dc.rights.spa.fl_str_mv Derechos reservados - Universidad Nacional de Colombia
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv Atribución-NoComercial 4.0 Internacional
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial 4.0 Internacional
Derechos reservados - Universidad Nacional de Colombia
http://creativecommons.org/licenses/by-nc/4.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Universidad Nacional de Colombia Sede Medellín
institution Universidad Nacional de Colombia
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