Ellipse Meets Bit-Planes: A Novel Approach to RNFL based Glaucoma Detection Using Advanced Image Processing and Deep Learning

2026-06-14

AI Summary

This study addresses the challenge of analyzing retinal nerve fiber layer (RNFL) due to positional variations of the optic disc and macula by proposing a dual-framework automated glaucoma detection method based on elliptical polar coordinate transformation. The approach integrates adaptive elliptical polar coordinate transformation with deep learning-based feature fusion to construct a high-accuracy model, while also incorporating bit-plane slicing image processing to develop a lightweight alternative. Experimental results demonstrate that the high-accuracy framework achieves a detection rate of 99.3%, and the lightweight framework attains an accuracy of 92.31%. By balancing performance with computational efficiency, the proposed method offers a scalable and cost-effective solution for early glaucoma screening.

Abstract

This work proposes an integrated pipeline for automatic glaucoma detection method from easily available colour fundas images based on an adaptive algorithm for ellipse-based polar transformation, to enhance the analysis of the Retinal Nerve Fiber Layer (RNFL) as the primary biomarker for observing glaucomatous changes, regardless of optic disc and macula position. Utilizing this transformation, we introduce two distinct frameworks tailored to different operational needs. The first framework, a deep learning-inspired feature fusion approach, achieves a 99.3% detection rate, ideal for settings where high precision is essential, despite higher computational demands. The second framework employs a novel image-processing algorithm based on bit-plane slicing, offering 92.31% accuracy and optimized for environments requiring rapid inference with minimal resource consumption. Both frameworks provide scalable and cost-effective solutions for early glaucoma detection. This study highlights the potential of RNFL-based diagnostic tools in addressing the global challenge of glaucoma, particularly in underserved regions.

Problem

• glaucoma detection
• Retinal Nerve Fiber Layer
• fundus images
• early diagnosis
• automated screening

Innovation

• ellipse-based polar transformation
• Retinal Nerve Fiber Layer (RNFL)
• bit-plane slicing
• deep learning feature fusion
• glaucoma detection