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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-03-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2022.858327/full |
| _version_ | 1818306924691587072 |
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| author | Surbhi Bhatia Shadab Alam Mohammed Shuaib Mohammed Hameed Alhameed Fathe Jeribi Razan Ibrahim Alsuwailem |
| author_facet | Surbhi Bhatia Shadab Alam Mohammed Shuaib Mohammed Hameed Alhameed Fathe Jeribi Razan Ibrahim Alsuwailem |
| author_sort | Surbhi Bhatia |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-13T06:50:13Z |
| format | Article |
| id | doaj.art-9a8d7fcd10e340e2a27a69e303d20aad |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-12-13T06:50:13Z |
| publishDate | 2022-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-9a8d7fcd10e340e2a27a69e303d20aad2022-12-21T23:56:10ZengFrontiers Media S.A.Frontiers in Public Health2296-25652022-03-011010.3389/fpubh.2022.858327858327Retinal Vessel Extraction via Assisted Multi-Channel Feature Map and U-NetSurbhi Bhatia0Shadab Alam1Mohammed Shuaib2Mohammed Hameed Alhameed3Fathe Jeribi4Razan Ibrahim Alsuwailem5Department of Information Systems, College of Computer Sciences and Information Technology, King Faisal University, Hofuf, Saudi ArabiaCollege of Computer Science and Information Technology, Jazan University, Jazan, Saudi ArabiaCollege of Computer Science and Information Technology, Jazan University, Jazan, Saudi ArabiaCollege of Computer Science and Information Technology, Jazan University, Jazan, Saudi ArabiaCollege of Computer Science and Information Technology, Jazan University, Jazan, Saudi ArabiaDepartment of Information Systems, College of Computer Sciences and Information Technology, King Faisal University, Hofuf, Saudi ArabiaEarly 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.https://www.frontiersin.org/articles/10.3389/fpubh.2022.858327/fullmultichannelretinal vesselsretinopathyU-NetCauchy distribution |
| spellingShingle | Surbhi Bhatia Shadab Alam Mohammed Shuaib Mohammed Hameed Alhameed Fathe Jeribi Razan Ibrahim Alsuwailem Retinal Vessel Extraction via Assisted Multi-Channel Feature Map and U-Net Frontiers in Public Health multichannel retinal vessels retinopathy U-Net Cauchy distribution |
| 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 | multichannel retinal vessels retinopathy U-Net Cauchy distribution |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2022.858327/full |
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