Detection and removal of dust artifacts in retinal images via sparse-based inpainting

Dust particle artifacts are present in all imaging modalities but have more adverse consequences in medical images like retinal images. They could be mistaken as small lesions, such as microaneurysms. We propose a method for detecting and accurately segmenting dust artifacts in retinal images based...

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Autores:: Barrios, Erik
Sierra, Enrique
Romero, Lenny A.
Millán, María S.
Marrugo, Andres G.

Tipo de recurso:

Fecha de publicación:: 2021

Institución:: Universidad Tecnológica de Bolívar

Repositorio:: Repositorio Institucional UTB

Idioma:: eng

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dc.title.spa.fl_str_mv	Detection and removal of dust artifacts in retinal images via sparse-based inpainting
title	Detection and removal of dust artifacts in retinal images via sparse-based inpainting
spellingShingle	Detection and removal of dust artifacts in retinal images via sparse-based inpainting Retina Image; Ophthalmology; Diabetic Retinopathy LEMB
title_short	Detection and removal of dust artifacts in retinal images via sparse-based inpainting
title_full	Detection and removal of dust artifacts in retinal images via sparse-based inpainting
title_fullStr	Detection and removal of dust artifacts in retinal images via sparse-based inpainting
title_full_unstemmed	Detection and removal of dust artifacts in retinal images via sparse-based inpainting
title_sort	Detection and removal of dust artifacts in retinal images via sparse-based inpainting
dc.creator.fl_str_mv	Barrios, Erik Sierra, Enrique Romero, Lenny A. Millán, María S. Marrugo, Andres G.
dc.contributor.author.none.fl_str_mv	Barrios, Erik Sierra, Enrique Romero, Lenny A. Millán, María S. Marrugo, Andres G.
dc.subject.keywords.spa.fl_str_mv	Retina Image; Ophthalmology; Diabetic Retinopathy
topic	Retina Image; Ophthalmology; Diabetic Retinopathy LEMB
dc.subject.armarc.none.fl_str_mv	LEMB
description	Dust particle artifacts are present in all imaging modalities but have more adverse consequences in medical images like retinal images. They could be mistaken as small lesions, such as microaneurysms. We propose a method for detecting and accurately segmenting dust artifacts in retinal images based on multi-scale template-matching on several input images and an iterative segmentation via an inpainting approach. The inpainting is done through dictionary learning and sparse-based representation. The artifact segmentation is refined by comparing the original image to the initial restoration. On average, 90% of the dust artifacts were detected in the test images, with state-of-theart restoration results. All detected artifacts were accurately segmented and removed. Even the most challenging artifacts located on top of blood vessels were removed. Thus, ensuring the continuity of the retinal structures. The proposed method successfully detects and removes dust artifacts in retinal images, which could be used to avoid false-positive lesion detections or as an image quality criterion. An implementation of the proposed algorithm can be accessed and executed through a Code Ocean compute capsule. © 2021. All Rights Reserved.
publishDate	2021
dc.date.issued.none.fl_str_mv	2021
dc.date.accessioned.none.fl_str_mv	2023-07-21T16:20:40Z
dc.date.available.none.fl_str_mv	2023-07-21T16:20:40Z
dc.date.submitted.none.fl_str_mv	2023
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dc.identifier.citation.spa.fl_str_mv	Barrios Montes, E. M., Sierra, E., Romero Pérez, L. A., Millán Garcia-Varela, M. S., & Marrugo Hernandez, A. G. (2021). Detection and removal of dust artifacts in retinal images via sparse-based inpainting. Óptica pura y aplicada, 54(3), 1-14.
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dc.identifier.doi.none.fl_str_mv	10.7149/OPA.54.3.51060
dc.identifier.instname.spa.fl_str_mv	Universidad Tecnológica de Bolívar
dc.identifier.reponame.spa.fl_str_mv	Repositorio Universidad Tecnológica de Bolívar
identifier_str_mv	Barrios Montes, E. M., Sierra, E., Romero Pérez, L. A., Millán Garcia-Varela, M. S., & Marrugo Hernandez, A. G. (2021). Detection and removal of dust artifacts in retinal images via sparse-based inpainting. Óptica pura y aplicada, 54(3), 1-14. 10.7149/OPA.54.3.51060 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar
url	https://hdl.handle.net/20.500.12585/12319
dc.language.iso.spa.fl_str_mv	eng
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dc.format.extent.none.fl_str_mv	14 páginas
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dc.publisher.place.spa.fl_str_mv	Cartagena de Indias
dc.source.spa.fl_str_mv	Optica Pura y Aplicada
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spelling	Barrios, Erikbb277699-e10e-4f85-982c-9a3b98acb515Sierra, Enriqued24332ba-a9f2-4a63-a549-840840da9ba0Romero, Lenny A.4e34aa8a-f981-4e1d-ae32-d45acb6abcf9Millán, María S.ddefb810-3364-48af-9963-b4070fe39d5dMarrugo, Andres G.3d6cd388-d48f-4669-934f-49ca4179f5422023-07-21T16:20:40Z2023-07-21T16:20:40Z20212023Barrios Montes, E. M., Sierra, E., Romero Pérez, L. A., Millán Garcia-Varela, M. S., & Marrugo Hernandez, A. G. (2021). Detection and removal of dust artifacts in retinal images via sparse-based inpainting. Óptica pura y aplicada, 54(3), 1-14.https://hdl.handle.net/20.500.12585/1231910.7149/OPA.54.3.51060Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarDust particle artifacts are present in all imaging modalities but have more adverse consequences in medical images like retinal images. They could be mistaken as small lesions, such as microaneurysms. We propose a method for detecting and accurately segmenting dust artifacts in retinal images based on multi-scale template-matching on several input images and an iterative segmentation via an inpainting approach. The inpainting is done through dictionary learning and sparse-based representation. The artifact segmentation is refined by comparing the original image to the initial restoration. On average, 90% of the dust artifacts were detected in the test images, with state-of-theart restoration results. All detected artifacts were accurately segmented and removed. Even the most challenging artifacts located on top of blood vessels were removed. Thus, ensuring the continuity of the retinal structures. The proposed method successfully detects and removes dust artifacts in retinal images, which could be used to avoid false-positive lesion detections or as an image quality criterion. An implementation of the proposed algorithm can be accessed and executed through a Code Ocean compute capsule. © 2021. All Rights Reserved.14 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Optica Pura y AplicadaDetection and removal of dust artifacts in retinal images via sparse-based inpaintinginfo:eu-repo/semantics/articleinfo:eu-repo/semantics/drafthttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_2df8fbb1Retina Image;Ophthalmology;Diabetic RetinopathyLEMBCartagena de IndiasAbramoff, M.D., Garvin, M.K., Sonka, M. Retinal imaging and image analysis (2010) IEEE Reviews in Biomedical Engineering, 3, art. no. 5660089, pp. 169-208. Cited 967 times. doi: 10.1109/RBME.2010.2084567Fenner, B.J., Wong, R.L.M., Lam, W.-C., Tan, G.S.W., Cheung, G.C.M. Advances in Retinal Imaging and Applications in Diabetic Retinopathy Screening: A Review (2018) Ophthalmology and Therapy, 7 (2), pp. 333-346. Cited 65 times. http://www.springer.com/springer+healthcare/journal/40123 doi: 10.1007/s40123-018-0153-7Marrugo, A.G., Millán, M.S. Retinal image analysis: Image processing and feature extraction oriented to the clinical task (2017) Optica Pura y Aplicada, 50 (1), pp. 49-62. Cited 3 times. http://www.sedoptica.es/Menu_Volumenes/Pdfs/OPA_50_1_49507.pdf doi: 10.7149/OPA.50.1.49507Sierra, E., Marrugo, A.G., Millán, M.S. Dust particle artifact detection and removal in retinal images (2017) Optica Pura y Aplicada, 50 (4), pp. 379-387. Cited 4 times. http://www.sedoptica.es/Menu_Volumenes/Pdfs/OPA_50_4_49075.pdf doi: 10.7149/OPA.50.4.49075Marrugo, A.G., Millán, M.S., Šorel, M., Šroubek, F. Restoration of retinal images with space-variant blur (2014) Journal of Biomedical Optics, 19 (1), art. no. 016023. 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Detection and removal of dust artifacts in retinal images via sparse-based inpainting

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