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Detection and Mosaicing Techniques for Low-Quality Retinal Videos
Ideally, to carry out screening for eye diseases, it is expected to use specialized medical equipment to capture retinal fundus images. However, since this kind of equipment is generally expensive and has low portability, and with the development of technology and the emergence of smartphones, new p...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8915098/ https://www.ncbi.nlm.nih.gov/pubmed/35271205 http://dx.doi.org/10.3390/s22052059 |
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author | Camara, José Silva, Bruno Gouveia, António Pires, Ivan Miguel Coelho, Paulo Cunha, António |
author_facet | Camara, José Silva, Bruno Gouveia, António Pires, Ivan Miguel Coelho, Paulo Cunha, António |
author_sort | Camara, José |
collection | PubMed |
description | Ideally, to carry out screening for eye diseases, it is expected to use specialized medical equipment to capture retinal fundus images. However, since this kind of equipment is generally expensive and has low portability, and with the development of technology and the emergence of smartphones, new portable and cheaper screening options have emerged, one of them being the D-Eye device. When compared to specialized equipment, this equipment and other similar devices associated with a smartphone present lower quality and less field-of-view in the retinal video captured, yet with sufficient quality to perform a medical pre-screening. Individuals can be referred for specialized screening to obtain a medical diagnosis if necessary. Two methods were proposed to extract the relevant regions from these lower-quality videos (the retinal zone). The first one is based on classical image processing approaches such as thresholds and Hough Circle transform. The other performs the extraction of the retinal location by applying a neural network, which is one of the methods reported in the literature with good performance for object detection, the YOLO v4, which was demonstrated to be the preferred method to apply. A mosaicing technique was implemented from the relevant retina regions to obtain a more informative single image with a higher field of view. It was divided into two stages: the GLAMpoints neural network was applied to extract relevant points in the first stage. Some homography transformations are carried out to have in the same referential the overlap of common regions of the images. In the second stage, a smoothing process was performed in the transition between images. |
format | Online Article Text |
id | pubmed-8915098 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-89150982022-03-12 Detection and Mosaicing Techniques for Low-Quality Retinal Videos Camara, José Silva, Bruno Gouveia, António Pires, Ivan Miguel Coelho, Paulo Cunha, António Sensors (Basel) Article Ideally, to carry out screening for eye diseases, it is expected to use specialized medical equipment to capture retinal fundus images. However, since this kind of equipment is generally expensive and has low portability, and with the development of technology and the emergence of smartphones, new portable and cheaper screening options have emerged, one of them being the D-Eye device. When compared to specialized equipment, this equipment and other similar devices associated with a smartphone present lower quality and less field-of-view in the retinal video captured, yet with sufficient quality to perform a medical pre-screening. Individuals can be referred for specialized screening to obtain a medical diagnosis if necessary. Two methods were proposed to extract the relevant regions from these lower-quality videos (the retinal zone). The first one is based on classical image processing approaches such as thresholds and Hough Circle transform. The other performs the extraction of the retinal location by applying a neural network, which is one of the methods reported in the literature with good performance for object detection, the YOLO v4, which was demonstrated to be the preferred method to apply. A mosaicing technique was implemented from the relevant retina regions to obtain a more informative single image with a higher field of view. It was divided into two stages: the GLAMpoints neural network was applied to extract relevant points in the first stage. Some homography transformations are carried out to have in the same referential the overlap of common regions of the images. In the second stage, a smoothing process was performed in the transition between images. MDPI 2022-03-07 /pmc/articles/PMC8915098/ /pubmed/35271205 http://dx.doi.org/10.3390/s22052059 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Camara, José Silva, Bruno Gouveia, António Pires, Ivan Miguel Coelho, Paulo Cunha, António Detection and Mosaicing Techniques for Low-Quality Retinal Videos |
title | Detection and Mosaicing Techniques for Low-Quality Retinal Videos |
title_full | Detection and Mosaicing Techniques for Low-Quality Retinal Videos |
title_fullStr | Detection and Mosaicing Techniques for Low-Quality Retinal Videos |
title_full_unstemmed | Detection and Mosaicing Techniques for Low-Quality Retinal Videos |
title_short | Detection and Mosaicing Techniques for Low-Quality Retinal Videos |
title_sort | detection and mosaicing techniques for low-quality retinal videos |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8915098/ https://www.ncbi.nlm.nih.gov/pubmed/35271205 http://dx.doi.org/10.3390/s22052059 |
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