ArticleJournal of healthcare engineering2017
Blood Vessel Extraction in Color Retinal Fundus Images with Enhancement Filtering and Unsupervised Classification.
Article in Journal of healthcare engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 42 citations in OpenAlex.
- Topology-preserving, diameter-specific framework for conjunctival vessel segmentation and tortuosity analysis in diabetes.Scientific reports · 2026Article
- Colour tone of retinal arterioles imaged with a colour scanning laser ophthalmoscope can be an indicator of systemic arterial stiffness.BMJ open ophthalmology · 2023Article
- A hybrid approach for diagnosing diabetic retinopathy from fundus image exploiting deep features.Heliyon · 2023Article
- Use of Black-and-White Digital Filters to Optimize Visualization in Cataract Surgery.Journal of clinical medicine · 2022Article
- Multiscale Joint Optimization Strategy for Retinal Vascular Segmentation.Sensors (Basel, Switzerland) · 2022Article
- Analysis of Vessel Segmentation Based on Various Enhancement Techniques for Improvement of Vessel Intensity Profile.Computational intelligence and neuroscience · 2022Article
- Hybrid Model Structure for Diabetic Retinopathy Classification.Journal of healthcare engineering · 2020Article
- A Novel Multiscale Gaussian-Matched Filter Using Neural Networks for the Segmentation of X-Ray Coronary Angiograms.Journal of healthcare engineering · 2018Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Retinal blood vessels have a significant role in the diagnosis and treatment of various retinal diseases such as diabetic retinopathy, glaucoma, arteriosclerosis, and hypertension. For this reason, retinal vasculature extraction is important in order to help specialists for the diagnosis and treatment of systematic diseases. In this paper, a novel approach is developed to extract retinal blood vessel network. Our method comprises four stages: (1) preprocessing stage in order to prepare dataset for segmentation; (2) an enhancement procedure including Gabor, Frangi, and Gauss filters obtained separately before a top-hat transform; (3) a hard and soft clustering stage which includes K-means and Fuzzy C-means (FCM) in order to get binary vessel map; and (4) a postprocessing step which removes falsely segmented isolated regions. The method is tested on color retinal images obtained from STARE and DRIVE databases which are available online. As a result, Gabor filter followed by K-means clustering method achieves 95.94% and 95.71% of accuracy for STARE and DRIVE databases, respectively, which are acceptable for diagnosis systems.
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