N., Tariq and S.M., Khaniabadi and S.S., Teoh and S.L., Wang and T.F., Ng and Hamzah, Rostam Affendi and Z., Embong and H., Ibrahim (2024) Literature survey on edge detection-based methods for blood vessel segmentation from retinal fundus images. In: Proceedings of the 12th International Conference on Robotics, Vision, Signal Processing and Power Applications.
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Literature Survey on Edge Detection-Based Methods for Blood Vessel Segmentation from Retinal Fundus Images.pdf Restricted to Registered users only Download (102kB) |
Abstract
Retinal vessel segmentation is an essential step in the diagnosis of various retinal diseases. Edge detection-based methods have shown promising results for retinal vessel segmentation due to their ability to identify the boundaries of the vessels. In this paper, we surveyed several edge detection-based methods for retinal vessel segmentation from three main databases: PubMed, IEEExplore, and Google Scholar. The outcomes from the literature search were filtered based on inclusion and exclusion criteria. From the selected literature, information about the edge detection techniques, the image datasets used, and the evaluation measures, are extracted. From this literature survey, we can see that there are many approaches that have been proposed by researchers to segment the blood vessel edges from the retinal fundus images. Most of them are using the traditional approaches, such as Sobel operators, and Canny edge detector. Recently, deep learning-based approaches have been proposed for this purpose. Some of the commonly used databases for retinal fundus images have also been reported in this review. Several evaluation measures that have been utilized by researchers have also been identified.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | Edge detector, Retinal vessel segmentation, Fundus images |
| Divisions: | Faculty Of Electronics And Computer Technology And Engineering |
| Depositing User: | NUR FARISAH JAFRIN |
| Date Deposited: | 15 Jun 2026 03:28 |
| Last Modified: | 15 Jun 2026 03:28 |
| URI: | http://eprints.utem.edu.my/id/eprint/29722 |
| Statistic Details: | View Download Statistic |
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