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Retinal Vessel Extraction via Assisted Multi-Channel Feature Map and U-Net
Early detection of vessels from fundus images can effectively prevent the permanent retinal damages caused by retinopathies such as glaucoma, hyperextension, and diabetes. Concerning the red color of both retinal vessels and background and the vessel's morphological variations, the current vess...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8968759/ https://www.ncbi.nlm.nih.gov/pubmed/35372222 http://dx.doi.org/10.3389/fpubh.2022.858327 |
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author | Bhatia, Surbhi Alam, Shadab Shuaib, Mohammed Hameed Alhameed, Mohammed Jeribi, Fathe Alsuwailem, Razan Ibrahim |
author_facet | Bhatia, Surbhi Alam, Shadab Shuaib, Mohammed Hameed Alhameed, Mohammed Jeribi, Fathe Alsuwailem, Razan Ibrahim |
author_sort | Bhatia, Surbhi |
collection | PubMed |
description | Early detection of vessels from fundus images can effectively prevent the permanent retinal damages caused by retinopathies such as glaucoma, hyperextension, and diabetes. Concerning the red color of both retinal vessels and background and the vessel's morphological variations, the current vessel detection methodologies fail to segment thin vessels and discriminate them in the regions where permanent retinopathies mainly occur. This research aims to suggest a novel approach to take the benefit of both traditional template-matching methods with recent deep learning (DL) solutions. These two methods are combined in which the response of a Cauchy matched filter is used to replace the noisy red channel of the fundus images. Consequently, a U-shaped fully connected convolutional neural network (U-net) is employed to train end-to-end segmentation of pixels into vessel and background classes. Each preprocessed image is divided into several patches to provide enough training images and speed up the training per each instance. The DRIVE public database has been analyzed to test the proposed method, and metrics such as Accuracy, Precision, Sensitivity and Specificity have been measured for evaluation. The evaluation indicates that the average extraction accuracy of the proposed model is 0.9640 on the employed dataset. |
format | Online Article Text |
id | pubmed-8968759 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-89687592022-04-01 Retinal Vessel Extraction via Assisted Multi-Channel Feature Map and U-Net Bhatia, Surbhi Alam, Shadab Shuaib, Mohammed Hameed Alhameed, Mohammed Jeribi, Fathe Alsuwailem, Razan Ibrahim Front Public Health Public Health Early detection of vessels from fundus images can effectively prevent the permanent retinal damages caused by retinopathies such as glaucoma, hyperextension, and diabetes. Concerning the red color of both retinal vessels and background and the vessel's morphological variations, the current vessel detection methodologies fail to segment thin vessels and discriminate them in the regions where permanent retinopathies mainly occur. This research aims to suggest a novel approach to take the benefit of both traditional template-matching methods with recent deep learning (DL) solutions. These two methods are combined in which the response of a Cauchy matched filter is used to replace the noisy red channel of the fundus images. Consequently, a U-shaped fully connected convolutional neural network (U-net) is employed to train end-to-end segmentation of pixels into vessel and background classes. Each preprocessed image is divided into several patches to provide enough training images and speed up the training per each instance. The DRIVE public database has been analyzed to test the proposed method, and metrics such as Accuracy, Precision, Sensitivity and Specificity have been measured for evaluation. The evaluation indicates that the average extraction accuracy of the proposed model is 0.9640 on the employed dataset. Frontiers Media S.A. 2022-03-17 /pmc/articles/PMC8968759/ /pubmed/35372222 http://dx.doi.org/10.3389/fpubh.2022.858327 Text en Copyright © 2022 Bhatia, Alam, Shuaib, Hameed Alhameed, Jeribi and Alsuwailem. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Public Health Bhatia, Surbhi Alam, Shadab Shuaib, Mohammed Hameed Alhameed, Mohammed Jeribi, Fathe Alsuwailem, Razan Ibrahim Retinal Vessel Extraction via Assisted Multi-Channel Feature Map and U-Net |
title | Retinal Vessel Extraction via Assisted Multi-Channel Feature Map and U-Net |
title_full | Retinal Vessel Extraction via Assisted Multi-Channel Feature Map and U-Net |
title_fullStr | Retinal Vessel Extraction via Assisted Multi-Channel Feature Map and U-Net |
title_full_unstemmed | Retinal Vessel Extraction via Assisted Multi-Channel Feature Map and U-Net |
title_short | Retinal Vessel Extraction via Assisted Multi-Channel Feature Map and U-Net |
title_sort | retinal vessel extraction via assisted multi-channel feature map and u-net |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8968759/ https://www.ncbi.nlm.nih.gov/pubmed/35372222 http://dx.doi.org/10.3389/fpubh.2022.858327 |
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