A color fusion model based on Markowitz portfolio optimization for optic disc segmentation in retinal images

Retinal disorders are a severe health threat for older adults because they may lead to vision loss and blindness. Diabetic patients are particularly prone to suffer from Diabetic Retinopathy. Identifying relevant structural components in color fundus images like the optic disc (OD) is crucial to dia...

Full description

Autores:
Escorcia-Gutierrez, Jose
Torrents-Barrena, Jordina
Gamarra, Margarita
Romero-Aroca, Pedro
Valls, Aida
Puig, Domenec
Tipo de recurso:
http://purl.org/coar/resource_type/c_816b
Fecha de publicación:
2021
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/8083
Acceso en línea:
https://hdl.handle.net/11323/8083
https://doi.org/10.1016/j.eswa.2021.114697
https://repositorio.cuc.edu.co/
Palabra clave:
Diabetic retinopathy
Optic disc
Color fusión
Markowitz portfolio
Segmentation
Rights
openAccess
License
CC0 1.0 Universal
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oai_identifier_str oai:repositorio.cuc.edu.co:11323/8083
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv A color fusion model based on Markowitz portfolio optimization for optic disc segmentation in retinal images
title A color fusion model based on Markowitz portfolio optimization for optic disc segmentation in retinal images
spellingShingle A color fusion model based on Markowitz portfolio optimization for optic disc segmentation in retinal images
Diabetic retinopathy
Optic disc
Color fusión
Markowitz portfolio
Segmentation
title_short A color fusion model based on Markowitz portfolio optimization for optic disc segmentation in retinal images
title_full A color fusion model based on Markowitz portfolio optimization for optic disc segmentation in retinal images
title_fullStr A color fusion model based on Markowitz portfolio optimization for optic disc segmentation in retinal images
title_full_unstemmed A color fusion model based on Markowitz portfolio optimization for optic disc segmentation in retinal images
title_sort A color fusion model based on Markowitz portfolio optimization for optic disc segmentation in retinal images
dc.creator.fl_str_mv Escorcia-Gutierrez, Jose
Torrents-Barrena, Jordina
Gamarra, Margarita
Romero-Aroca, Pedro
Valls, Aida
Puig, Domenec
dc.contributor.author.spa.fl_str_mv Escorcia-Gutierrez, Jose
Torrents-Barrena, Jordina
Gamarra, Margarita
Romero-Aroca, Pedro
Valls, Aida
Puig, Domenec
dc.subject.spa.fl_str_mv Diabetic retinopathy
Optic disc
Color fusión
Markowitz portfolio
Segmentation
topic Diabetic retinopathy
Optic disc
Color fusión
Markowitz portfolio
Segmentation
description Retinal disorders are a severe health threat for older adults because they may lead to vision loss and blindness. Diabetic patients are particularly prone to suffer from Diabetic Retinopathy. Identifying relevant structural components in color fundus images like the optic disc (OD) is crucial to diagnose retinal diseases. Automatic OD detection is complex because of its location in an area where blood vessels converge, and color distribution is uneven. Several image processing techniques have been developed for OD detection so far, but vessel segmentation is sometimes required, increasing computational complexity and time. Moreover, precise OD segmentation methods utilize complex algorithms that need special hardware or extensive labeled datasets. We propose an OD detection approach based on the Modern Portfolio Theory of Markowitz to generate an innovative color fusion model. Specifically, the training phase calculates the optimal weights for each color channel. A fusion of weighted color channels is then applied in the testing phase. This approach acts as a powerful and real-time preprocessing stage. We use four heterogeneous datasets to validate the presented methodology. Three out of four datasets are publicly available (i.e., DRIVE, Messidor, and HRF), and the last corresponds to an in–house dataset acquired from Hospital Universitari Sant Joan de Reus (Spain). Two different segmentation methods are presented and compared with state-of-the-art computer vision techniques to analyze the model performance. An outstanding accuracy and overlap above 0.9 and 80%, respectively, and a minimal execution time of 0.05 s are reached. Therefore, our model could be integrated into daily clinical practice to accelerate the diagnosis of Diabetic Retinopathy due to its simplicity, performance, and speed.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-04-07T13:52:18Z
dc.date.available.none.fl_str_mv 2021-04-07T13:52:18Z
dc.date.issued.none.fl_str_mv 2021-07-15
dc.date.embargoEnd.none.fl_str_mv 2023-07-19
dc.type.spa.fl_str_mv Pre-Publicación
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_816b
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/preprint
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dc.identifier.issn.spa.fl_str_mv 0957-4174
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/8083
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1016/j.eswa.2021.114697
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 0957-4174
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/8083
https://doi.org/10.1016/j.eswa.2021.114697
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.rights.spa.fl_str_mv CC0 1.0 Universal
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/publicdomain/zero/1.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
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rights_invalid_str_mv CC0 1.0 Universal
http://creativecommons.org/publicdomain/zero/1.0/
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eu_rights_str_mv openAccess
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Corporación Universidad de la Costa
dc.source.spa.fl_str_mv Expert Systems with Applications
institution Corporación Universidad de la Costa
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spelling Escorcia-Gutierrez, JoseTorrents-Barrena, JordinaGamarra, MargaritaRomero-Aroca, PedroValls, AidaPuig, Domenec2021-04-07T13:52:18Z2021-04-07T13:52:18Z2021-07-152023-07-190957-4174https://hdl.handle.net/11323/8083https://doi.org/10.1016/j.eswa.2021.114697Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Retinal disorders are a severe health threat for older adults because they may lead to vision loss and blindness. Diabetic patients are particularly prone to suffer from Diabetic Retinopathy. Identifying relevant structural components in color fundus images like the optic disc (OD) is crucial to diagnose retinal diseases. Automatic OD detection is complex because of its location in an area where blood vessels converge, and color distribution is uneven. Several image processing techniques have been developed for OD detection so far, but vessel segmentation is sometimes required, increasing computational complexity and time. Moreover, precise OD segmentation methods utilize complex algorithms that need special hardware or extensive labeled datasets. We propose an OD detection approach based on the Modern Portfolio Theory of Markowitz to generate an innovative color fusion model. Specifically, the training phase calculates the optimal weights for each color channel. A fusion of weighted color channels is then applied in the testing phase. This approach acts as a powerful and real-time preprocessing stage. We use four heterogeneous datasets to validate the presented methodology. Three out of four datasets are publicly available (i.e., DRIVE, Messidor, and HRF), and the last corresponds to an in–house dataset acquired from Hospital Universitari Sant Joan de Reus (Spain). Two different segmentation methods are presented and compared with state-of-the-art computer vision techniques to analyze the model performance. An outstanding accuracy and overlap above 0.9 and 80%, respectively, and a minimal execution time of 0.05 s are reached. 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