Using Brightness Distribution in Fundus Images to Automated Glaucoma Detection

Glaucoma is a condition of the optic system that can cause vision loss as a result of increased intraocular pressure. The disease is becoming more prevalent, and its diagnosis can be both costly and time-consuming. One prominent area of the retina affected by glaucoma is the optic nerve head, which undergoes structural changes. The goal of the study was to detect glaucoma by analyzing the brightness distribution in optic nerve head fundus images, with statistical parameters serving as the primary means of measurement. Specifically, the mean and standard deviation were extracted as features from the fundus images, representing the central tendency and variation of data within the images. The proposed approach demonstrated highly accurate results, outperforming existing literature in the field.