Automatic Segmentation of Exudates in Ocular Images using Ensembles of Aperture Filters and Logistic Regression

Autores
Benalcazar Palacios, Marco Enrique; Brun, Marcel; Ballarin, Virginia Laura
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Hard and soft exudates are the main signs of diabetic macular edema (DME). The segmentation of both kinds of exudates generates valuable information not only for the diagnosis of DME, but also for treatment, which helps to avoid vision loss and blindness. In this paper, we propose a new algorithm for the automatic segmentation of exudates in ocular fundus images. The proposed algorithm is based on ensembles of aperture filters that detect exudate candidates and remove major blood vessels from the processed images. Then, logistic regression is used to classify each candidate as either exudate or non-exudate based on a vector of 31 features that characterize each potensial lesion. Finally, we tested the performance of the proposed algorithm using the images in the public HEI-MED database.
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; Argentina
Fil: Ballarin, Virginia Laura. Universidad Nacional de Mar del Plata; Argentina
Materia
Aperture Filters
Logistic Regression
Ensembles of Classifiers
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:11336/25904
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Automatic Segmentation of Exudates in Ocular Images using Ensembles of Aperture Filters and Logistic RegressionBenalcazar Palacios, Marco EnriqueBrun, MarcelBallarin, Virginia LauraAperture FiltersLogistic RegressionEnsembles of Classifiershttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Hard and soft exudates are the main signs of diabetic macular edema (DME). The segmentation of both kinds of exudates generates valuable information not only for the diagnosis of DME, but also for treatment, which helps to avoid vision loss and blindness. In this paper, we propose a new algorithm for the automatic segmentation of exudates in ocular fundus images. The proposed algorithm is based on ensembles of aperture filters that detect exudate candidates and remove major blood vessels from the processed images. Then, logistic regression is used to classify each candidate as either exudate or non-exudate based on a vector of 31 features that characterize each potensial lesion. Finally, we tested the performance of the proposed algorithm using the images in the public HEI-MED database.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; ArgentinaFil: Ballarin, Virginia Laura. Universidad Nacional de Mar del Plata; ArgentinaIOPScience2013-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/25904Benalcazar Palacios, Marco Enrique; Brun, Marcel; Ballarin, Virginia Laura; Automatic Segmentation of Exudates in Ocular Images using Ensembles of Aperture Filters and Logistic Regression; IOPScience; Journal of Physics: Conference Series; 477; 1; 10-20131742-65881742-6596CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1088/1742-6596/477/1/012021info:eu-repo/semantics/altIdentifier/url/http://iopscience.iop.org/article/10.1088/1742-6596/477/1/012021/metainfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:21:10Zoai:ri.conicet.gov.ar:11336/25904instacron: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 10:21:11.167CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Automatic Segmentation of Exudates in Ocular Images using Ensembles of Aperture Filters and Logistic Regression
title Automatic Segmentation of Exudates in Ocular Images using Ensembles of Aperture Filters and Logistic Regression
spellingShingle Automatic Segmentation of Exudates in Ocular Images using Ensembles of Aperture Filters and Logistic Regression
Benalcazar Palacios, Marco Enrique
Aperture Filters
Logistic Regression
Ensembles of Classifiers
title_short Automatic Segmentation of Exudates in Ocular Images using Ensembles of Aperture Filters and Logistic Regression
title_full Automatic Segmentation of Exudates in Ocular Images using Ensembles of Aperture Filters and Logistic Regression
title_fullStr Automatic Segmentation of Exudates in Ocular Images using Ensembles of Aperture Filters and Logistic Regression
title_full_unstemmed Automatic Segmentation of Exudates in Ocular Images using Ensembles of Aperture Filters and Logistic Regression
title_sort Automatic Segmentation of Exudates in Ocular Images using Ensembles of Aperture Filters and Logistic Regression
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 Aperture Filters
Logistic Regression
Ensembles of Classifiers
topic Aperture Filters
Logistic Regression
Ensembles of Classifiers
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Hard and soft exudates are the main signs of diabetic macular edema (DME). The segmentation of both kinds of exudates generates valuable information not only for the diagnosis of DME, but also for treatment, which helps to avoid vision loss and blindness. In this paper, we propose a new algorithm for the automatic segmentation of exudates in ocular fundus images. The proposed algorithm is based on ensembles of aperture filters that detect exudate candidates and remove major blood vessels from the processed images. Then, logistic regression is used to classify each candidate as either exudate or non-exudate based on a vector of 31 features that characterize each potensial lesion. Finally, we tested the performance of the proposed algorithm using the images in the public HEI-MED database.
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; Argentina
Fil: Ballarin, Virginia Laura. Universidad Nacional de Mar del Plata; Argentina
description Hard and soft exudates are the main signs of diabetic macular edema (DME). The segmentation of both kinds of exudates generates valuable information not only for the diagnosis of DME, but also for treatment, which helps to avoid vision loss and blindness. In this paper, we propose a new algorithm for the automatic segmentation of exudates in ocular fundus images. The proposed algorithm is based on ensembles of aperture filters that detect exudate candidates and remove major blood vessels from the processed images. Then, logistic regression is used to classify each candidate as either exudate or non-exudate based on a vector of 31 features that characterize each potensial lesion. Finally, we tested the performance of the proposed algorithm using the images in the public HEI-MED database.
publishDate 2013
dc.date.none.fl_str_mv 2013-10
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/11336/25904
Benalcazar Palacios, Marco Enrique; Brun, Marcel; Ballarin, Virginia Laura; Automatic Segmentation of Exudates in Ocular Images using Ensembles of Aperture Filters and Logistic Regression; IOPScience; Journal of Physics: Conference Series; 477; 1; 10-2013
1742-6588
1742-6596
CONICET Digital
CONICET
url http://hdl.handle.net/11336/25904
identifier_str_mv Benalcazar Palacios, Marco Enrique; Brun, Marcel; Ballarin, Virginia Laura; Automatic Segmentation of Exudates in Ocular Images using Ensembles of Aperture Filters and Logistic Regression; IOPScience; Journal of Physics: Conference Series; 477; 1; 10-2013
1742-6588
1742-6596
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1088/1742-6596/477/1/012021
info:eu-repo/semantics/altIdentifier/url/http://iopscience.iop.org/article/10.1088/1742-6596/477/1/012021/meta
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 IOPScience
publisher.none.fl_str_mv IOPScience
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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