Glaucoma is an eye disorder that is categorized by elevated Intraocular Pressure (IOP). This increased IOP leads to damage to the optic nerve head. If it is will lead to loss of vision. Glaucoma will be detected by extraction of the optic cup (OC) and optic disc (OD), that is, the no of pixels in the optic disc and optic cup are calculated. In our project, we automatically extract the optic disc in retinal images by pixel-based segmentation and optic cup by mathematical morphology and watershed transformation. In the existing method, the optic cup and optic disc are segmented by creating a mask manually, and from that glaucoma is detected. But its experimental result doesn’t close to the clinical CDR value. But in our proposed system, we don’t need to select the OD and OC boundary by creating the mask. The OD is the high-intensity part of an eye. So, we easily and simply extract the disc boundary by the thresholding used in pixel-based segmentation. Cup segmentation is much more challenging compared to disc segmentation due to the presence of high-density vascular architecture in the region of the optic cup traversing the cup boundary. Also, the transition between the cup and the surrounding neuro retinal rim may be gradual and decrease the visibility of the cup boundary. But, in the proposed system watershed segmentation easily isolate the OD and OC boundary. The optic cup to optic disc ratio is calculated, to show the progression of glaucoma.