In this thesis, an automated, computerized technique for facilitating the detection of glaucoma from fundus images is proposed and demonstrated. This process utilizes such techniques as pyramidal stereo-matching, fuzzy clustering, active contours, feature extraction, and others. High accuracy is achieved as evaluated through sensitivity and specificity calculations of the regions of interest within the digital fundus images. Furthermore, a review of such a system's weaknesses, necessary improvements, and areas of future research are included to complete the investigation.