The shape and appearance of the optic nerve head region are sensitive to changes associated with glaucoma and diabetes that may be otherwise asymptomatic. The changes can be diagnostic of the diseases, and tracking of the changes in sequential images can be used to assess treatment and the progress of the illness. At present, change detection and tracking are performed manually, which can be a cause of poor repeatability. We are concerned with developing automated techniques of generating quantitative descriptions of the retinal images that might be used in diagnosis and assessment. In this paper, we investigate the use of images that have been collected and stored remotely, as this will replicate capture and automated processing by outreach clinics. Normal and abnormal images were collected from a range of sources, to simulate the mass screening process. The images were processed using simple signal-processing methods and divided into two groups. Using a chi-squared test, the separation of normal and abnormal images using this test was found to be highly significant (p < 0.05, n = 60).