Automatic design of aperture filters using neural networks applied to ocular image segmentation

Autores
Benalcazar Palacios, Marco Enrique; Brun, Marcel; Ballarin, Virginia Laura
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Aperture filters are image operators which combine mathematical morphology and pattern recognition theory to design windowed classifiers. Previous works propose designing and representing such operators using large decision tables and classic linear pattern classifiers. These approaches demand an enormous computational cost in order to solve real image problems. The current work presents a new method to automatically design Aperture filters for color and grayscale image processing. This approach consists of designing a family of Aperture filters using artificial feed-forward neural networks. The resulting Aperture filters are combined into a single one using an ensemble method. The performance of the proposed approach was evaluated by segmenting blood vessels in ocular images of the DRIVE database. The results show the suitability of this approach: It outperforms window operators designed using neural networks and logistic regression as well as Aperture filters designed using logistic regression and support vector machines.
Fil: Benalcazar Palacios, Marco Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Secretaría Nacional de Educación Superior, Ciencia Tecnología e Innovación; Ecuador
Fil: Brun, Marcel. Universidad Nacional de Mar del Plata. Facultad de Ingeniería; Argentina
Fil: Ballarin, Virginia Laura. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Mar del Plata. Facultad de Ingeniería; Argentina
Materia
Apertures
Artificial Neural Networks
Image Segmentation
Training
Gray-Scale
Biomedical Imaging
Blood Vessels
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:11336/34876
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/34876
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Automatic design of aperture filters using neural networks applied to ocular image segmentationBenalcazar Palacios, Marco EnriqueBrun, MarcelBallarin, Virginia LauraAperturesArtificial Neural NetworksImage SegmentationTrainingGray-ScaleBiomedical ImagingBlood Vesselshttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Aperture filters are image operators which combine mathematical morphology and pattern recognition theory to design windowed classifiers. Previous works propose designing and representing such operators using large decision tables and classic linear pattern classifiers. These approaches demand an enormous computational cost in order to solve real image problems. The current work presents a new method to automatically design Aperture filters for color and grayscale image processing. This approach consists of designing a family of Aperture filters using artificial feed-forward neural networks. The resulting Aperture filters are combined into a single one using an ensemble method. The performance of the proposed approach was evaluated by segmenting blood vessels in ocular images of the DRIVE database. The results show the suitability of this approach: It outperforms window operators designed using neural networks and logistic regression as well as Aperture filters designed using logistic regression and support vector machines.Fil: Benalcazar Palacios, Marco Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Secretaría Nacional de Educación Superior, Ciencia Tecnología e Innovación; EcuadorFil: Brun, Marcel. Universidad Nacional de Mar del Plata. Facultad de Ingeniería; ArgentinaFil: Ballarin, Virginia Laura. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Mar del Plata. Facultad de Ingeniería; ArgentinaEuropean Association for Signal Processing2014-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/34876Benalcazar Palacios, Marco Enrique; Brun, Marcel; Ballarin, Virginia Laura; Automatic design of aperture filters using neural networks applied to ocular image segmentation; European Association for Signal Processing; European Signal Processing Conference; 22; 9-2014; 2195-21992219-5491CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://ieeexplore.ieee.org/document/6952799/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:37:55Zoai:ri.conicet.gov.ar:11336/34876instacron: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:34982025-10-15 14:37:55.568CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Automatic design of aperture filters using neural networks applied to ocular image segmentation
title Automatic design of aperture filters using neural networks applied to ocular image segmentation
spellingShingle Automatic design of aperture filters using neural networks applied to ocular image segmentation
Benalcazar Palacios, Marco Enrique
Apertures
Artificial Neural Networks
Image Segmentation
Training
Gray-Scale
Biomedical Imaging
Blood Vessels
title_short Automatic design of aperture filters using neural networks applied to ocular image segmentation
title_full Automatic design of aperture filters using neural networks applied to ocular image segmentation
title_fullStr Automatic design of aperture filters using neural networks applied to ocular image segmentation
title_full_unstemmed Automatic design of aperture filters using neural networks applied to ocular image segmentation
title_sort Automatic design of aperture filters using neural networks applied to ocular image segmentation
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 Apertures
Artificial Neural Networks
Image Segmentation
Training
Gray-Scale
Biomedical Imaging
Blood Vessels
topic Apertures
Artificial Neural Networks
Image Segmentation
Training
Gray-Scale
Biomedical Imaging
Blood Vessels
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Aperture filters are image operators which combine mathematical morphology and pattern recognition theory to design windowed classifiers. Previous works propose designing and representing such operators using large decision tables and classic linear pattern classifiers. These approaches demand an enormous computational cost in order to solve real image problems. The current work presents a new method to automatically design Aperture filters for color and grayscale image processing. This approach consists of designing a family of Aperture filters using artificial feed-forward neural networks. The resulting Aperture filters are combined into a single one using an ensemble method. The performance of the proposed approach was evaluated by segmenting blood vessels in ocular images of the DRIVE database. The results show the suitability of this approach: It outperforms window operators designed using neural networks and logistic regression as well as Aperture filters designed using logistic regression and support vector machines.
Fil: Benalcazar Palacios, Marco Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Secretaría Nacional de Educación Superior, Ciencia Tecnología e Innovación; Ecuador
Fil: Brun, Marcel. Universidad Nacional de Mar del Plata. Facultad de Ingeniería; Argentina
Fil: Ballarin, Virginia Laura. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Mar del Plata. Facultad de Ingeniería; Argentina
description Aperture filters are image operators which combine mathematical morphology and pattern recognition theory to design windowed classifiers. Previous works propose designing and representing such operators using large decision tables and classic linear pattern classifiers. These approaches demand an enormous computational cost in order to solve real image problems. The current work presents a new method to automatically design Aperture filters for color and grayscale image processing. This approach consists of designing a family of Aperture filters using artificial feed-forward neural networks. The resulting Aperture filters are combined into a single one using an ensemble method. The performance of the proposed approach was evaluated by segmenting blood vessels in ocular images of the DRIVE database. The results show the suitability of this approach: It outperforms window operators designed using neural networks and logistic regression as well as Aperture filters designed using logistic regression and support vector machines.
publishDate 2014
dc.date.none.fl_str_mv 2014-09
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/11336/34876
Benalcazar Palacios, Marco Enrique; Brun, Marcel; Ballarin, Virginia Laura; Automatic design of aperture filters using neural networks applied to ocular image segmentation; European Association for Signal Processing; European Signal Processing Conference; 22; 9-2014; 2195-2199
2219-5491
CONICET Digital
CONICET
url http://hdl.handle.net/11336/34876
identifier_str_mv Benalcazar Palacios, Marco Enrique; Brun, Marcel; Ballarin, Virginia Laura; Automatic design of aperture filters using neural networks applied to ocular image segmentation; European Association for Signal Processing; European Signal Processing Conference; 22; 9-2014; 2195-2199
2219-5491
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://ieeexplore.ieee.org/document/6952799/
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
application/pdf
dc.publisher.none.fl_str_mv European Association for Signal Processing
publisher.none.fl_str_mv European Association for Signal Processing
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
_version_ 1846082855071907840
score 13.22299

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