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Glaucoma is a major cause of blindness globally. It damages the optic nerve cells that transmit visual information to the brain. Intra-Ocular Pressure (IOP) is the most significant danger reason to develop glaucoma. Even though a number of variables, including various optic disc parameters, have been used to discover early glaucoma damage, there is a real need for computer-aided detection (CAD) methods that can identify early glaucomatous development so that treatment to avoid further progression can be started especially in mass screenings, because ophthalmologists have limited time to assess mass number of fundus images.This thesis focused on the description of a system based on image processing and classification techniques for the estimation of quantitative parameters to classify fundus images into two classes: glaucoma patients and normal patients.