Automatic Design of Window Operators for the Segmentation of the Prostate Gland in Magnetic Resonance Images

Autores
Benalcazar Palacios, Marco Enrique; Brun, Marcel; Ballarin, Virginia Laura
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
W-operators are nonlinear image operators that are translation invariant and locally defined inside a finite spatial window. In this work, we consider the problem of automatic design of W-operators for the segmentation of magnetic resonance (MR) volumes as a problem of classifier design. We propose to segment the objects of interest in an MR volume by classifying each pixel of its slices as either part of the objects of interest or background. The classifiers used here are the artificial feed-forward neural networks. The proposed method is applied to the segmentation of the two main regions of the prostate gland: the peripheral zone and the central gland. Performance evaluation was carried out on the volumes of the Prostate-3T collection of the NCI-ISBI 2013 Challenge. The results obtained show the suitability of our approach as a marker detector of the prostate gland.
Fil: Benalcazar Palacios, Marco Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Mar del Plata; Argentina. Secretaría Nacional de Educación Superior, Ciencia, Tecnología e Innovación. Guayaquil; Ecuador
Fil: Brun, Marcel. Universidad Nacional de Mar del Plata; Argentina
Fil: Ballarin, Virginia Laura. Universidad Nacional de Mar del Plata; Argentina
Materia
W-Operator
Segmentation
Magnetic Resonance
Prostate Gland
Feed-Forward Neural Network
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/34830
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/34830
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Automatic Design of Window Operators for the Segmentation of the Prostate Gland in Magnetic Resonance ImagesBenalcazar Palacios, Marco EnriqueBrun, MarcelBallarin, Virginia LauraW-OperatorSegmentationMagnetic ResonanceProstate GlandFeed-Forward Neural Networkhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1W-operators are nonlinear image operators that are translation invariant and locally defined inside a finite spatial window. In this work, we consider the problem of automatic design of W-operators for the segmentation of magnetic resonance (MR) volumes as a problem of classifier design. We propose to segment the objects of interest in an MR volume by classifying each pixel of its slices as either part of the objects of interest or background. The classifiers used here are the artificial feed-forward neural networks. The proposed method is applied to the segmentation of the two main regions of the prostate gland: the peripheral zone and the central gland. Performance evaluation was carried out on the volumes of the Prostate-3T collection of the NCI-ISBI 2013 Challenge. The results obtained show the suitability of our approach as a marker detector of the prostate gland.Fil: Benalcazar Palacios, Marco Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Mar del Plata; Argentina. Secretaría Nacional de Educación Superior, Ciencia, Tecnología e Innovación. Guayaquil; EcuadorFil: Brun, Marcel. Universidad Nacional de Mar del Plata; ArgentinaFil: Ballarin, Virginia Laura. Universidad Nacional de Mar del Plata; ArgentinaSpringer2014-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/34830Benalcazar Palacios, Marco Enrique; Brun, Marcel; Ballarin, Virginia Laura; Automatic Design of Window Operators for the Segmentation of the Prostate Gland in Magnetic Resonance Images; Springer; Ifmbe Proceedings; 49; 10-2014; 417-4201680-0737CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-13117-7_107info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/chapter/10.1007%2F978-3-319-13117-7_107info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:08:35Zoai:ri.conicet.gov.ar:11336/34830instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:08:35.825CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Automatic Design of Window Operators for the Segmentation of the Prostate Gland in Magnetic Resonance Images
title Automatic Design of Window Operators for the Segmentation of the Prostate Gland in Magnetic Resonance Images
spellingShingle Automatic Design of Window Operators for the Segmentation of the Prostate Gland in Magnetic Resonance Images
Benalcazar Palacios, Marco Enrique
W-Operator
Segmentation
Magnetic Resonance
Prostate Gland
Feed-Forward Neural Network
title_short Automatic Design of Window Operators for the Segmentation of the Prostate Gland in Magnetic Resonance Images
title_full Automatic Design of Window Operators for the Segmentation of the Prostate Gland in Magnetic Resonance Images
title_fullStr Automatic Design of Window Operators for the Segmentation of the Prostate Gland in Magnetic Resonance Images
title_full_unstemmed Automatic Design of Window Operators for the Segmentation of the Prostate Gland in Magnetic Resonance Images
title_sort Automatic Design of Window Operators for the Segmentation of the Prostate Gland in Magnetic Resonance Images
dc.creator.none.fl_str_mv Benalcazar Palacios, Marco Enrique
Brun, Marcel
Ballarin, Virginia Laura
author Benalcazar Palacios, Marco Enrique
author_facet Benalcazar Palacios, Marco Enrique
Brun, Marcel
Ballarin, Virginia Laura
author_role author
author2 Brun, Marcel
Ballarin, Virginia Laura
author2_role author
author
dc.subject.none.fl_str_mv W-Operator
Segmentation
Magnetic Resonance
Prostate Gland
Feed-Forward Neural Network
topic W-Operator
Segmentation
Magnetic Resonance
Prostate Gland
Feed-Forward Neural Network
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv W-operators are nonlinear image operators that are translation invariant and locally defined inside a finite spatial window. In this work, we consider the problem of automatic design of W-operators for the segmentation of magnetic resonance (MR) volumes as a problem of classifier design. We propose to segment the objects of interest in an MR volume by classifying each pixel of its slices as either part of the objects of interest or background. The classifiers used here are the artificial feed-forward neural networks. The proposed method is applied to the segmentation of the two main regions of the prostate gland: the peripheral zone and the central gland. Performance evaluation was carried out on the volumes of the Prostate-3T collection of the NCI-ISBI 2013 Challenge. The results obtained show the suitability of our approach as a marker detector of the prostate gland.
Fil: Benalcazar Palacios, Marco Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Mar del Plata; Argentina. Secretaría Nacional de Educación Superior, Ciencia, Tecnología e Innovación. Guayaquil; Ecuador
Fil: Brun, Marcel. Universidad Nacional de Mar del Plata; Argentina
Fil: Ballarin, Virginia Laura. Universidad Nacional de Mar del Plata; Argentina
description W-operators are nonlinear image operators that are translation invariant and locally defined inside a finite spatial window. In this work, we consider the problem of automatic design of W-operators for the segmentation of magnetic resonance (MR) volumes as a problem of classifier design. We propose to segment the objects of interest in an MR volume by classifying each pixel of its slices as either part of the objects of interest or background. The classifiers used here are the artificial feed-forward neural networks. The proposed method is applied to the segmentation of the two main regions of the prostate gland: the peripheral zone and the central gland. Performance evaluation was carried out on the volumes of the Prostate-3T collection of the NCI-ISBI 2013 Challenge. The results obtained show the suitability of our approach as a marker detector of the prostate gland.
publishDate 2014
dc.date.none.fl_str_mv 2014-10
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/34830
Benalcazar Palacios, Marco Enrique; Brun, Marcel; Ballarin, Virginia Laura; Automatic Design of Window Operators for the Segmentation of the Prostate Gland in Magnetic Resonance Images; Springer; Ifmbe Proceedings; 49; 10-2014; 417-420
1680-0737
CONICET Digital
CONICET
url http://hdl.handle.net/11336/34830
identifier_str_mv Benalcazar Palacios, Marco Enrique; Brun, Marcel; Ballarin, Virginia Laura; Automatic Design of Window Operators for the Segmentation of the Prostate Gland in Magnetic Resonance Images; Springer; Ifmbe Proceedings; 49; 10-2014; 417-420
1680-0737
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-13117-7_107
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/chapter/10.1007%2F978-3-319-13117-7_107
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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