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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/34876
Ver los metadatos del registro completo
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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 Técnicas2025-09-29T09:44:25Zoai: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:34982025-09-29 09:44:25.743CONICET 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 |
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13.070432 |