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Comprehensive review of retinal blood vessel segmentation and classification techniques: intelligent solutions for green computing in medical images, current challenges, open issues, and knowledge gaps in fundus medical images

Aldehydes; Blood; Blood vessels; Classification (of information); Deep learning; Eye protection; Image classification; Image segmentation; Learning algorithms; Learning systems; Medical imaging; Blood-vessel segmentations; Deep learning; Fundus image; Machine learning techniques; Retinal blood; Reti...

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Main Authors: Abdulsahib A.A., Mahmoud M.A., Mohammed M.A., Rasheed H.H., Mostafa S.A., Maashi M.S.
Other Authors: 57222592694
Format: Review
Published: Springer 2023
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spelling my.uniten.dspace-258952023-05-29T17:05:28Z Comprehensive review of retinal blood vessel segmentation and classification techniques: intelligent solutions for green computing in medical images, current challenges, open issues, and knowledge gaps in fundus medical images Abdulsahib A.A. Mahmoud M.A. Mohammed M.A. Rasheed H.H. Mostafa S.A. Maashi M.S. 57222592694 55247787300 57192089894 57205684160 37036085800 57216199758 Aldehydes; Blood; Blood vessels; Classification (of information); Deep learning; Eye protection; Image classification; Image segmentation; Learning algorithms; Learning systems; Medical imaging; Blood-vessel segmentations; Deep learning; Fundus image; Machine learning techniques; Retinal blood; Retinal blood vessel segmentation and retinal blood vessel classification; Retinal blood vessels; Retinal vessels; Vessel classification; Ophthalmology; adult; clinical assessment; deep learning; eye fundus; illumination; photography; retina blood vessel; retina image; review Recently, there has been an advancement in the development of innovative computer-aided techniques for the segmentation and classification of retinal vessels, the application of which is predominant in clinical applications. Consequently, this study aims to provide a detailed overview of the techniques available for segmentation and classification of retinal vessels. Initially, retinal fundus photography and retinal image patterns are briefly introduced. Then, an introduction to the pre-processing operations and advanced methods of identifying retinal vessels is deliberated. In addition, a discussion on the validation stage and assessment of the outcomes of retinal vessels segmentation is presented. In this paper, the proposed methods of classifying arteries and veins in fundus images are extensively reviewed, which are categorized into automatic and semi-automatic categories. There are some challenges associated with the classification of vessels in images of the retinal fundus, which include the low contrast accompanying the fundus image and the inhomogeneity of the background lighting. The inhomogeneity occurs as a result of the process of imaging, whereas the low contrast which accompanies the image is caused by the variation between the background and the contrast of the various blood vessels. This means that the contrast of thicker vessels is higher than those that are thinner. Another challenge is related to the color changes that occur in the retina from different subjects, which are rooted in biological features. Most of the techniques used for the classification of the retinal vessels are based on geometric and visual characteristics that set the veins apart from the arteries. In this study, different major contributions are summarized as review studies that adopted deep learning approaches and machine learning techniques to address each of the limitations and problems in retinal blood vessel segmentation and classification techniques. We also review the current challenges, knowledge gaps and open issues, limitations and problems in retinal blood vessel segmentation and classification techniques. � 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, AT part of Springer Nature. Final 2023-05-29T09:05:28Z 2023-05-29T09:05:28Z 2021 Review 10.1007/s13721-021-00294-7 2-s2.0-85103347231 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103347231&doi=10.1007%2fs13721-021-00294-7&partnerID=40&md5=4a2d2f487aa638699ca3f15ee1d704cd https://irepository.uniten.edu.my/handle/123456789/25895 10 1 20 Springer Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Aldehydes; Blood; Blood vessels; Classification (of information); Deep learning; Eye protection; Image classification; Image segmentation; Learning algorithms; Learning systems; Medical imaging; Blood-vessel segmentations; Deep learning; Fundus image; Machine learning techniques; Retinal blood; Retinal blood vessel segmentation and retinal blood vessel classification; Retinal blood vessels; Retinal vessels; Vessel classification; Ophthalmology; adult; clinical assessment; deep learning; eye fundus; illumination; photography; retina blood vessel; retina image; review
author2 57222592694
author_facet 57222592694
Abdulsahib A.A.
Mahmoud M.A.
Mohammed M.A.
Rasheed H.H.
Mostafa S.A.
Maashi M.S.
format Review
author Abdulsahib A.A.
Mahmoud M.A.
Mohammed M.A.
Rasheed H.H.
Mostafa S.A.
Maashi M.S.
spellingShingle Abdulsahib A.A.
Mahmoud M.A.
Mohammed M.A.
Rasheed H.H.
Mostafa S.A.
Maashi M.S.
Comprehensive review of retinal blood vessel segmentation and classification techniques: intelligent solutions for green computing in medical images, current challenges, open issues, and knowledge gaps in fundus medical images
author_sort Abdulsahib A.A.
title Comprehensive review of retinal blood vessel segmentation and classification techniques: intelligent solutions for green computing in medical images, current challenges, open issues, and knowledge gaps in fundus medical images
title_short Comprehensive review of retinal blood vessel segmentation and classification techniques: intelligent solutions for green computing in medical images, current challenges, open issues, and knowledge gaps in fundus medical images
title_full Comprehensive review of retinal blood vessel segmentation and classification techniques: intelligent solutions for green computing in medical images, current challenges, open issues, and knowledge gaps in fundus medical images
title_fullStr Comprehensive review of retinal blood vessel segmentation and classification techniques: intelligent solutions for green computing in medical images, current challenges, open issues, and knowledge gaps in fundus medical images
title_full_unstemmed Comprehensive review of retinal blood vessel segmentation and classification techniques: intelligent solutions for green computing in medical images, current challenges, open issues, and knowledge gaps in fundus medical images
title_sort comprehensive review of retinal blood vessel segmentation and classification techniques: intelligent solutions for green computing in medical images, current challenges, open issues, and knowledge gaps in fundus medical images
publisher Springer
publishDate 2023
_version_ 1806428078726971392
score 13.214268

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