ArticleSensors (Basel, Switzerland)2021
Interactive Blood Vessel Segmentation from Retinal Fundus Image Based on Canny Edge Detector.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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Who cites it
8 citing papers in PubMed, 52 citations in OpenAlex.
- Pixel-Attention W-Shaped Network for Joint Lesion Segmentation and Diabetic Retinopathy Severity Staging.Diagnostics (Basel, Switzerland) · 2025Article
- A Robust Blood Vessel Segmentation Technique for Angiographic Images Employing Multi-Scale Filtering Approach.Journal of clinical medicine · 2025Article
- An innovative methodology for segmenting vessel like structures using artificial intelligence and image processing.Scientific reports · 2024Article
- Alterations in Macular Microvasculature in Pterygium Patients Measured by OCT Angiography.Diagnostics (Basel, Switzerland) · 2023Article
- [A survey of loss function of medical image segmentation algorithms].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- Brain Tumor Segmentation Based on Bendlet Transform and Improved Chan-Vese Model.Entropy (Basel, Switzerland) · 2022Article
- LightEyes: A Lightweight Fundus Segmentation Network for Mobile Edge Computing.Sensors (Basel, Switzerland) · 2022Article
- Retinal Vessel Segmentation Based on B-COSFIRE Filters in Fundus Images.Frontiers in public health · 2022Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
Optometrists, ophthalmologists, orthoptists, and other trained medical professionals use fundus photography to monitor the progression of certain eye conditions or diseases. Segmentation of the vessel tree is an essential process of retinal analysis. In this paper, an interactive blood vessel segmentation from retinal fundus image based on Canny edge detection is proposed. Semi-automated segmentation of specific vessels can be done by simply moving the cursor across a particular vessel. The pre-processing stage includes the green color channel extraction, applying Contrast Limited Adaptive Histogram Equalization (CLAHE), and retinal outline removal. After that, the edge detection techniques, which are based on the Canny algorithm, will be applied. The vessels will be selected interactively on the developed graphical user interface (GUI). The program will draw out the vessel edges. After that, those vessel edges will be segmented to bring focus on its details or detect the abnormal vessel. This proposed approach is useful because different edge detection parameter settings can be applied to the same image to highlight particular vessels for analysis or presentation.
Indexed as
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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.