Assessment of sparse-based inpainting for retinal vessel removal

[EN] Some important eye diseases, like macular degeneration or diabetic retinopathy, can induce changes visible on the retina, for example as lesions. Segmentation of lesions or extraction of textural features from the fundus images are possible steps towards automatic detection of such diseases whi...

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Detalhes bibliográficos
Autores: Colomer, Adrián|||0000-0002-7616-6029, Naranjo Ornedo, Valeriana|||0000-0002-0181-3412, Engan, Kjersti, Skretting, Karl
Formato: artículo
Fecha de publicación:2017
País:España
Recursos:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/148099
Acesso em linha:https://riunet.upv.es/handle/10251/148099
Access Level:acceso abierto
Palavra-chave:Sparse-based inpainting
Blood vessel removal
Image inpainting
Inpainting quality evaluation index
Diffusion-based inpainting
Non-artificial inpainting
TEORIA DE LA SEÑAL Y COMUNICACIONES
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spelling Assessment of sparse-based inpainting for retinal vessel removalColomer, Adrián|||0000-0002-7616-6029Naranjo Ornedo, Valeriana|||0000-0002-0181-3412Engan, KjerstiSkretting, KarlSparse-based inpaintingBlood vessel removalImage inpaintingInpainting quality evaluation indexDiffusion-based inpaintingNon-artificial inpaintingTEORIA DE LA SEÑAL Y COMUNICACIONES[EN] Some important eye diseases, like macular degeneration or diabetic retinopathy, can induce changes visible on the retina, for example as lesions. Segmentation of lesions or extraction of textural features from the fundus images are possible steps towards automatic detection of such diseases which could facilitate screening as well as provide support for clinicians. For the task of detecting significant features, retinal blood vessels are considered as being interference on the retinal images. If these blood vessel structures could be suppressed, it might lead to a more accurate segmentation of retinal lesions as well as a better extraction of textural features to be used for pathology detection. This work proposes the use of sparse representations and dictionary learning techniques for retinal vessel inpainting. The performance of the algorithm is tested for greyscale and RGB images from the DRIVE and STARE public databases, employing different neighbourhoods and sparseness factors. Moreover, a comparison with the most common inpainting family, diffusion-based methods, is carried out. For this purpose, two different ways of assessing the quality of the inpainting are presented and used to evaluate the results of the non-artificial inpainting, i.e. where a reference image does not exist. The results suggest that the use of sparse-based inpainting performs very well for retinal blood vessels removal which will be useful for the future detection and classification of eye diseases. (C) 2017 Elsevier B.V. All rights reserved.This work was supported by NILS Science and Sustainability Programme (014-ABEL-IM-2013) and by the Ministerio de Economia y Competitividad of Spain, Project ACRIMA (TIN2013-46751-R). The work of Adrian Colomer has been supported by the Spanish Government under the FPI Grant BES-2014-067889.ElsevierEscuela Técnica Superior de Ingeniería de TelecomunicaciónDepartamento de Estadística e Investigación Operativa Aplicadas y CalidadDepartamento de ComunicacionesInstituto Universitario de Investigación en Tecnología Centrada en el Ser HumanoEuropean CommissionEEA GrantsMinisterio de Economía y CompetitividadRepositorio Institucional de la Universitat Politècnica de València Riunet20172017-11-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://riunet.upv.es/handle/10251/148099reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengMinisterio de Economía y Competitividad http://dx.doi.org/10.13039/501100003329 TIN2013-46751-R ANALISIS DE IMAGEN DE FONDO DE OJO PARA CRIBADO AUTOMATICO DE ENFERMEDADES OFTALMOLOGICASopen accesshttp://purl.org/coar/access_right/c_abf2Reserva de todos los derechoshttp://rightsstatements.org/vocab/InC/1.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/1480992026-06-13T07:49:27Z
dc.title.none.fl_str_mv Assessment of sparse-based inpainting for retinal vessel removal
title Assessment of sparse-based inpainting for retinal vessel removal
spellingShingle Assessment of sparse-based inpainting for retinal vessel removal
Colomer, Adrián|||0000-0002-7616-6029
Sparse-based inpainting
Blood vessel removal
Image inpainting
Inpainting quality evaluation index
Diffusion-based inpainting
Non-artificial inpainting
TEORIA DE LA SEÑAL Y COMUNICACIONES
title_short Assessment of sparse-based inpainting for retinal vessel removal
title_full Assessment of sparse-based inpainting for retinal vessel removal
title_fullStr Assessment of sparse-based inpainting for retinal vessel removal
title_full_unstemmed Assessment of sparse-based inpainting for retinal vessel removal
title_sort Assessment of sparse-based inpainting for retinal vessel removal
dc.creator.none.fl_str_mv Colomer, Adrián|||0000-0002-7616-6029
Naranjo Ornedo, Valeriana|||0000-0002-0181-3412
Engan, Kjersti
Skretting, Karl
author Colomer, Adrián|||0000-0002-7616-6029
author_facet Colomer, Adrián|||0000-0002-7616-6029
Naranjo Ornedo, Valeriana|||0000-0002-0181-3412
Engan, Kjersti
Skretting, Karl
author_role author
author2 Naranjo Ornedo, Valeriana|||0000-0002-0181-3412
Engan, Kjersti
Skretting, Karl
author2_role author
author
author
dc.contributor.none.fl_str_mv Escuela Técnica Superior de Ingeniería de Telecomunicación
Departamento de Estadística e Investigación Operativa Aplicadas y Calidad
Departamento de Comunicaciones
Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano
European Commission
EEA Grants
Ministerio de Economía y Competitividad
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Sparse-based inpainting
Blood vessel removal
Image inpainting
Inpainting quality evaluation index
Diffusion-based inpainting
Non-artificial inpainting
TEORIA DE LA SEÑAL Y COMUNICACIONES
topic Sparse-based inpainting
Blood vessel removal
Image inpainting
Inpainting quality evaluation index
Diffusion-based inpainting
Non-artificial inpainting
TEORIA DE LA SEÑAL Y COMUNICACIONES
description [EN] Some important eye diseases, like macular degeneration or diabetic retinopathy, can induce changes visible on the retina, for example as lesions. Segmentation of lesions or extraction of textural features from the fundus images are possible steps towards automatic detection of such diseases which could facilitate screening as well as provide support for clinicians. For the task of detecting significant features, retinal blood vessels are considered as being interference on the retinal images. If these blood vessel structures could be suppressed, it might lead to a more accurate segmentation of retinal lesions as well as a better extraction of textural features to be used for pathology detection. This work proposes the use of sparse representations and dictionary learning techniques for retinal vessel inpainting. The performance of the algorithm is tested for greyscale and RGB images from the DRIVE and STARE public databases, employing different neighbourhoods and sparseness factors. Moreover, a comparison with the most common inpainting family, diffusion-based methods, is carried out. For this purpose, two different ways of assessing the quality of the inpainting are presented and used to evaluate the results of the non-artificial inpainting, i.e. where a reference image does not exist. The results suggest that the use of sparse-based inpainting performs very well for retinal blood vessels removal which will be useful for the future detection and classification of eye diseases. (C) 2017 Elsevier B.V. All rights reserved.
publishDate 2017
dc.date.none.fl_str_mv 2017
2017-11-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/148099
url https://riunet.upv.es/handle/10251/148099
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Ministerio de Economía y Competitividad http://dx.doi.org/10.13039/501100003329 TIN2013-46751-R ANALISIS DE IMAGEN DE FONDO DE OJO PARA CRIBADO AUTOMATICO DE ENFERMEDADES OFTALMOLOGICAS
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301629

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