Various retinal disorders, commonly diabetic and hypertensive retinopathy, can damage the optic nerve, potentially leading to permanent vision loss. Clinical observations often detect these conditions, such as abnormalities in retinal blood vessel diameter and the optic cup-to-disc ratio. High blood pressure can cause retinal vessel thinning and optic cup dilation, disrupting the normal arterio-venous ratio (AVR) and cup-to-disc ratio (CDR). This disruption may result in nerve fiber damage, hemorrhages, and cotton wool spots. This study proposes an automated retinal optic disk and vessel segmentation from pre-processed retinal images. The segmentation was done using a ring mask created by superimposing two circles with the optic disk center and radii of 3D/2 and 1.5D/2, where D denotes the diameter of the optic disk. The maximum AV crossing was avoided within the retinal mask to simplify the process. Validations were performed by comparing the results with a predefined manually segmented dataset, achieving accuracies of 98.6% and 97.8% for retinal optic disk and optic cup, respectively, and 98.73% for retinal vessels. This algorithm could aid ophthalmologists in identifying retinal disorders accurately and automatically.