Automated retinal nerve fiber layer defect detection using fundus imaging in glaucoma.
Rashmi Panda1, N B Puhan1, Aparna Rao2
1School of Electrical Sciences, IIT Bhubaneswar, India.
Summary
This study introduces a cost-effective method for detecting retinal nerve fiber layer defects (RNFLDs) using redfree fundus images. This approach aids in early glaucoma risk assessment by automatically identifying structural changes.
Area of Science:
- Ophthalmology
- Medical Imaging
- Computer Vision
Background:
- Retinal nerve fiber layer defects (RNFLDs) are early indicators of glaucoma.
- Current detection methods (OCT, GDx) are expensive for widespread use.
Purpose of the Study:
- To develop an automatic, cost-effective method for RNFLD detection and angular width quantification.
- To enable computer-assisted glaucoma risk assessment using redfree fundus images.
Main Methods:
- Image preprocessing: blood vessel inpainting and CLAHE enhancement.
- Boundary pixel identification: 1D intensity profiles and local minima analysis.
- Classification: Random Forest with CZC-LBP, DDE, entropy, and intensity features.
- Angular width quantification: RANSAC line fitting.
Main Results:
- Achieved high RNFLD detection performance on a new dataset.
- Sensitivity (SN) of 0.7821 at 0.2727 false positives per image (FPI).
- Area Under the Curve (AUC) of 0.8733.
Conclusions:
- The proposed method offers a practical and affordable approach for RNFLD detection.
- This technique can significantly contribute to early glaucoma diagnosis and management.
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