Glaucoma risk index: Automated glaucoma detection from color fundus images

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Disc-Aware Ensemble Network for Glaucoma Screening From Fundus Image (2018, methodology): “…with ﬁve state-of-the-art glaucoma screening baselines: wavelet-based feature method (Wavelet) in [15], Gabor transformation method (Gabor) in [17], Glau-coma risk index method (GRI) in [14], superpixel-based clas-siﬁcation method (Superpixel) in [10] and Deep Segmentation method (DeepCDR) in [12].”
CNNs for automatic glaucoma assessment using fundus images: an extensive validation (2019, methodology): “In a fundus image, the optic disc can be visually separated into two zones, a bright and central zone called optic cup and a peripheral part called neuro-retinal rim [3].”
Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database (2013, background): “…can be useful for diagnosis, such as the macula, which is usually examined in connection with age-related macular degeneration [1]; the optic disc for examination of glaucoma [2]; and vascular structures, which are mostly evaluated in the context of diseases affecting the circulatory system [3].”
Optic Disk and Cup Segmentation From Monocular Color Retinal Images for Glaucoma Assessment (2011, neutral): “two-class classification is employed to declare a given image as normal or glaucomatous [7]–[9], [3], [10]–[12].”