ArticleScientific reports2024
A new texture-based labeling framework for hyper-reflective foci identification in retinal optical coherence tomography images.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- U-Net Optimization for Hyperreflective Foci Segmentation in Retinal OCT.Diagnostics (Basel, Switzerland) · 2026Article
- Learning feature dependencies for precise tumor region detection and segmentation in optical coherence tomography images.International ophthalmology · 2025Article
- Hyperreflective Retinal Foci (HRF): Definition and Role of an Invaluable OCT Sign.Journal of clinical medicine · 2025Article
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4 authors.
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Abstract
An important abnormality in Optical Coherence Tomography (OCT) images is Hyper-Reflective Foci (HRF). This anomaly can be interpreted as a biomarker of serious retinal diseases such as Age-related Macular Degeneration (AMD) and Diabetic Macular Edema (DME) or the progression of disease from an early stage to a late one. In this paper, a new method is proposed for the identification of HRFs. The new method divides the OCT B-scan into patches and separately verifies each patch to determine whether or not the patch contains an HRF. The procedure of patch verification contains a texture-based framework which assigns appropriate labels according to intensity changes to each column and row. Then, a feature vector is extracted for each patch based on the assigned labels. The feature vectors are utilized in the training step of well-known classifiers like Support Vector Machine (SVM). Then, the classifiers are used to produce the labels for the test OCT images. The new method is evaluated on a public dataset including HRF labels. The experimental results show that the new method is capable of providing outstanding results in terms of speed and accuracy.
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