Publication:
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

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dc.citedby22
dc.contributor.authorAbdulsahib A.A.en_US
dc.contributor.authorMahmoud M.A.en_US
dc.contributor.authorMohammed M.A.en_US
dc.contributor.authorRasheed H.H.en_US
dc.contributor.authorMostafa S.A.en_US
dc.contributor.authorMaashi M.S.en_US
dc.contributor.authorid57222592694en_US
dc.contributor.authorid55247787300en_US
dc.contributor.authorid57192089894en_US
dc.contributor.authorid57205684160en_US
dc.contributor.authorid37036085800en_US
dc.contributor.authorid57216199758en_US
dc.date.accessioned2023-05-29T09:05:28Z
dc.date.available2023-05-29T09:05:28Z
dc.date.issued2021
dc.descriptionAldehydes; 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; reviewen_US
dc.description.abstractRecently, 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo20
dc.identifier.doi10.1007/s13721-021-00294-7
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85103347231
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85103347231&doi=10.1007%2fs13721-021-00294-7&partnerID=40&md5=4a2d2f487aa638699ca3f15ee1d704cd
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25895
dc.identifier.volume10
dc.publisherSpringeren_US
dc.sourceScopus
dc.sourcetitleNetwork Modeling Analysis in Health Informatics and Bioinformatics
dc.titleComprehensive 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 imagesen_US
dc.typeReviewen_US
dspace.entity.typePublication
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