ArticleJournal of healthcare engineering2017
The Edge Detectors Suitable for Retinal OCT Image Segmentation.
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 6 papers.
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Who cites it
6 citing papers in PubMed.
- Longitudinal Monitoring of Inflammatory Bowel Disease in Mice Using Endoscopic Optical Coherence Tomography.Inflammatory bowel diseases · 2025Article
- Recent Optical Coherence Tomography (OCT) Innovations for Increased Accessibility and Remote Surveillance.Bioengineering (Basel, Switzerland) · 2025Review
- Recent advances in the application of artificial intelligence in age-related macular degeneration.BMJ open ophthalmology · 2024Review
- Real-time closed-loop tissue-specific laser osteotomy using deep-learning-assisted optical coherence tomography.Biomedical optics express · 2023Article
- TranSegNet: Hybrid CNN-Vision Transformers Encoder for Retina Segmentation of Optical Coherence Tomography.Life (Basel, Switzerland) · 2023Article
- Wide-field quantitative micro-elastography of human breast tissue.Biomedical optics express · 2018Article
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5 authors.
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Abstract
Retinal layer thickness measurement offers important information for reliable diagnosis of retinal diseases and for the evaluation of disease development and medical treatment responses. This task critically depends on the accurate edge detection of the retinal layers in OCT images. Here, we intended to search for the most suitable edge detectors for the retinal OCT image segmentation task. The three most promising edge detection algorithms were identified in the related literature: Canny edge detector, the two-pass method, and the EdgeFlow technique. The quantitative evaluation results show that the two-pass method outperforms consistently the Canny detector and the EdgeFlow technique in delineating the retinal layer boundaries in the OCT images. In addition, the mean localization deviation metrics show that the two-pass method caused the smallest edge shifting problem. These findings suggest that the two-pass method is the best among the three algorithms for detecting retinal layer boundaries. The overall better performance of Canny and two-pass methods over EdgeFlow technique implies that the OCT images contain more intensity gradient information than texture changes along the retinal layer boundaries. The results will guide our future efforts in the quantitative analysis of retinal OCT images for the effective use of OCT technologies in the field of ophthalmology.
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