ArticlePloS one2014
Accurate image analysis of the retina using hessian matrix and binarisation of thresholded entropy with application of texture mapping.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Imaging the later-life white matter pathologies of repetitive head impacts: A novel pattern revealed through T2 FLAIR MRI.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Fractional Active Contour Model with Adaptive Hessian Weighting for Mammogram Segmentation.Journal of medical signals and sensors · 2026Article
- Texture-Based Preprocessing Framework with nnU-Net Model for Accurate Intracranial Artery Segmentation.Journal of imaging · 2025Article
- Intraoperative superb microvascular ultrasound imaging in glioma: novel quantitative analysis correlates with tumour grade.Acta neurochirurgica · 2025Article
- Effectiveness of radiology modalities in diagnosing and characterizing brain disorders.Neurosciences (Riyadh, Saudi Arabia) · 2024Article
- Digital Filtering Techniques Using Fuzzy-Rules Based Logic Control.Journal of imaging · 2023Review
- Enhancing fine retinal vessel segmentation: Morphological reconstruction and double thresholds filtering strategy.PloS one · 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
- Scale space detector for analyzing spatiotemporal ventricular contractility and nuclear morphogenesis in zebrafish.iScience · 2022Article
- A Review on the Rule-Based Filtering Structure with Applications on Computational Biomedical Images.Journal of healthcare engineering · 2022Review
- Translational health technology and system schemes: enhancing the dynamics of health informatics.Health information science and systems · 2020Article
- Automated detection of mild and multi-class diabetic eye diseases using deep learning.Health information science and systems · 2020Article
- Artery/vein classification of retinal vessels using classifiers fusion.Health information science and systems · 2019Article
- Retinal vessel segmentation using dense U-net with multiscale inputs.Journal of medical imaging (Bellingham, Wash.) · 2019Article
- Article
- Automatic and fast segmentation of breast region-of-interest (ROI) and density in MRIs.Heliyon · 2018Article
- Parametric model fitting-based approach for retinal blood vessel caliber estimation in eye fundus images.PloS one · 2018Article
- Blood vessel segmentation in color fundus images based on regional and Hessian features.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2017Review
- The Edge Detectors Suitable for Retinal OCT Image Segmentation.Journal of healthcare engineering · 2017Article
- A Review on Recent Developments for Detection of Diabetic Retinopathy.Scientifica · 2016Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, we demonstrate a comprehensive method for segmenting the retinal vasculature in camera images of the fundus. This is of interest in the area of diagnostics for eye diseases that affect the blood vessels in the eye. In a departure from other state-of-the-art methods, vessels are first pre-grouped together with graph partitioning, using a spectral clustering technique based on morphological features. Local curvature is estimated over the whole image using eigenvalues of Hessian matrix in order to enhance the vessels, which appear as ridges in images of the retina. The result is combined with a binarized image, obtained using a threshold that maximizes entropy, to extract the retinal vessels from the background. Speckle type noise is reduced by applying a connectivity constraint on the extracted curvature based enhanced image. This constraint is varied over the image according to each region's predominant blood vessel size. The resultant image exhibits the central light reflex of retinal arteries and veins, which prevents the segmentation of whole vessels. To address this, the earlier entropy-based binarization technique is repeated on the original image, but crucially, with a different threshold to incorporate the central reflex vessels. The final segmentation is achieved by combining the segmented vessels with and without central light reflex. We carry out our approach on DRIVE and REVIEW, two publicly available collections of retinal images for research purposes. The obtained results are compared with state-of-the-art methods in the literature using metrics such as sensitivity (true positive rate), selectivity (false positive rate) and accuracy rates for the DRIVE images and measured vessel widths for the REVIEW images. Our approach out-performs the methods in the literature.
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