Image analysis for diagnosis of Glaucoma using soft computing methods

Optic nerves are neurons that carry visual signals from the eyes to the brain. When this nerve is affected, it leads to various eye diseases. Glaucoma is a neurological disorder that damages the visual nerves. If glaucoma is not treated, it might cause irreversible blindness. A rise in intraocular pressure (IOP) in the optic nerve brought on by an inadequate outflow of excess fluid from the eyes is the primary cause of glaucoma. The goal of the proposed work is to use MATLAB graphical user interface image processing techniques to produce an automated design that can diagnose glaucoma earlier on. Initially, a dataset containing retinal fundus images is collected and pre-processed. The retinal disc and retinal cup boundaries are then determined by applying the morphological Hough transform. After elliptical fitting and threshold segmentation, the cup to disc ratio (CDR) score is calculated. The phases of glaucoma are then diagnosed by comparing the computed CDR value with the threshold value. This unique method has overcome the existing disadvantages by reducing the time for diagnosis.