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Updated: May 14, 2026
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Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
Published on: May 25, 2020
Anterior chamber angle classification using multiscale histograms of oriented gradients for glaucoma subtype
Yanwu Xu1, Jiang Liu, Ngan Meng Tan
1Institute for Infocomm Research, Agency for Science, Technology and Research, 138632, Singapore. yaxu@i2r.a-star.edu.sg
Summary
This study introduces an automated method using HOG features from OCT images to classify anterior chamber angles (ACA) as open or closed. The visual feature approach significantly improves glaucoma subtype classification accuracy.
Area of Science:
- Ophthalmology
- Medical Imaging
- Computer Vision
Background:
- Glaucoma diagnosis relies on anterior chamber angle (ACA) configuration.
- Accurate ACA classification is crucial for determining appropriate glaucoma treatment.
- Existing methods often depend on less precise clinical features.
Purpose of the Study:
- To develop and validate an automated ACA classification method using multiscale HOG features from OCT images.
- To compare the performance of visual features against traditional clinical features for ACA classification.
- To enhance the accuracy of differentiating open angles (OA) from angle-closure (AC) glaucoma subtypes.
Main Methods:
- Automated localization of the ACA in digital optical coherence tomography (OCT) images.
- Extraction of multiscale Histograms of Oriented Gradients (HOG) features from the localized ACA region.
- Classification of ACA as OA or AC using the extracted HOG visual features.
Main Results:
- The proposed method achieved an Area Under the Curve (AUC) of 0.835±0.068.
- Balanced accuracy reached 75.8% ± 6.4% at 85% specificity.
- Demonstrated superior performance compared to existing ACA classification approaches based on clinical features.
Conclusions:
- Automated ACA classification using multiscale HOG features is effective for glaucoma subtype identification.
- High-dimensional visual features outperform low-dimensional clinical features in ACA classification accuracy.
- This approach offers a more accurate and automated alternative for ACA assessment in clinical practice.
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