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.

翻译：本文提出了一种集成化流水线，用于根据易于获取的彩色眼底图像实现自动青光眼检测方法。该方法基于椭圆极坐标变换的自适应算法，旨在增强对视网膜神经纤维层（RNFL）——作为观察青光眼变化的主要生物标志物——的分析能力，且不受视盘和黄斑位置影响。利用这一变换，我们引入了两种针对不同操作需求定制的独特框架。第一框架采用基于深度学习的特征融合方法，实现了99.3%的检测率，在计算需求较高时仍能确保高精度，适用于对精确度要求严苛的场景。第二框架采用基于位平面分割的新型图像处理算法，准确率达92.31%，并针对需要快速推理且资源消耗最低的环境进行了优化。两种框架均为早期青光眼检测提供了可扩展且经济高效的解决方案。本研究凸显了基于RNFL的诊断工具在应对全球性青光眼挑战（尤其是在医疗资源匮乏地区）中的潜力。