ReviewJournal of medical signals and sensors
A Comprehensive Study of Retinal Vessel Classification Methods in Fundus Images.
Review in Journal of medical signals and sensors. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 63 citations in OpenAlex.
- A Robust Blood Vessel Segmentation Technique for Angiographic Images Employing Multi-Scale Filtering Approach.Journal of clinical medicine · 2025Article
- Research progress in deep learning-based fundus image analysis for the diagnosis and prediction of hypertension-related diseases.Frontiers in cell and developmental biology · 2025Review
- From Image to Sequence: Exploring Vision Transformers for Optical Coherence Tomography Classification.Journal of medical signals and sensors · 2025Article
- Bio-spectroscopic investigation linking changes of retinal structure with short-term administration of Amiodarone and revealing the ameliorative effect of vitamin E supplementation.Scientific reports · 2024Article
- Differential artery-vein analysis improves the OCTA classification of diabetic retinopathy.Biomedical optics express · 2024Article
- Non-Invasive Retinal Vessel Analysis as a Predictor for Cardiovascular Disease.Journal of personalized medicine · 2024Review
- Potential measurement error from vessel reflex and multiple light paths in dual-wavelength retinal oximetry.Acta ophthalmologica · 2024Article
- Effects of Gold Nanoparticles Phytoreduced with Rutin in an Early Rat Model of Diabetic Retinopathy and Cataracts.Metabolites · 2023Article
- Artificial Intelligence Frameworks to Detect and Investigate the Pathophysiology of Spaceflight Associated Neuro-Ocular Syndrome (SANS).Brain sciences · 2023Review
- An open-source deep learning network AVA-Net for arterial-venous area segmentation in optical coherence tomography angiography.Communications medicine · 2023Article
- Local-Sensitive Connectivity Filter (LS-CF): A Post-Processing Unsupervised Improvement of the Frangi, Hessian and Vesselness Filters for Multimodal Vessel Segmentation.Journal of imaging · 2022Article
- MF-AV-Net: an open-source deep learning network with multimodal fusion options for artery-vein segmentation in OCT angiography.Biomedical optics express · 2022Article
- Depth-resolved vascular profile features for artery-vein classification in OCT and OCT angiography of human retina.Biomedical optics express · 2022Article
- Diabetic and Hypertensive Retinopathy Screening in Fundus Images Using Artificially Intelligent Shallow Architectures.Journal of personalized medicine · 2021Article
- The Impact of Chronic Heart Failure on Retinal Vessel Density Assessed by Optical Coherence Tomography Angiography in Children with Dilated Cardiomyopathy.Journal of clinical medicine · 2021Article
- Differential artery-vein analysis in quantitative retinal imaging: a review.Quantitative imaging in medicine and surgery · 2021Review
- Retinal blood flow in critical illness and systemic disease: a review.Annals of intensive care · 2020Review
- Apigenin and Ethaverine Hydrochloride Enhance Retinal Vascular Barrier In Vitro and In Vivo.Translational vision science & technology · 2020Article
- Artificial Intelligence in Ophthalmology: A Meta-Analysis of Deep Learning Models for Retinal Vessels Segmentation.Journal of clinical medicine · 2020Article
- Segmentation of retinal blood vessels based on feature-oriented dictionary learning and sparse coding using ensemble classification approach.Journal of medical imaging (Bellingham, Wash.) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nowadays, it is obvious that there is a relationship between changes in the retinal vessel structure and diseases such as diabetic, hypertension, stroke, and the other cardiovascular diseases in adults as well as retinopathy of prematurity in infants. Retinal fundus images provide non-invasive visualization of the retinal vessel structure. Applying image processing techniques in the study of digital color fundus photographs and analyzing their vasculature is a reliable approach for early diagnosis of the aforementioned diseases. Reduction in the arteriolar-venular ratio of retina is one of the primary signs of hypertension, diabetic, and cardiovascular diseases which can be calculated by analyzing the fundus images. To achieve a precise measuring of this parameter and meaningful diagnostic results, accurate classification of arteries and veins is necessary. Classification of vessels in fundus images faces with some challenges that make it difficult. In this paper, a comprehensive study of the proposed methods for classification of arteries and veins in fundus images is presented. Considering that these methods are evaluated on different datasets and use different evaluation criteria, it is not possible to conduct a fair comparison of their performance. Therefore, we evaluate the classification methods from modeling perspective. This analysis reveals that most of the proposed approaches have focused on statistics, and geometric models in spatial domain and transform domain models have received less attention. This could suggest the possibility of using transform models, especially data adaptive ones, for modeling of the fundus images in future classification approaches.
Indexed as
Identifiers
28553578PMC5437764W2618884931What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.