Computer-aided diagnosis (CAD) has become a vital part of medical evaluations and pathology detection in the United States [1]. Systematic use of CAD systems since 1980s has caused a signi ficant change in the utilization of the computer output for pathology diagnosis, disease prognosis and treatment prioritization. Automated pathology detection and screening systems assist in interpretation of the medical signals, achieving a baseline evaluation and automatically analyzing disease severity, which in turn helps to prioritize patients for treatment follow-ups. Examples of popular CAD systems include analysis of X-ray, Magnetic Resonance Imaging (MRI) and Ultrasound images.
Medical images can be considered as 2-dimensional signals from a particular part of the human body that are accompanied by significant background noise from other neighboring parts of the body. Thus, automated analysis of medical images involves elegant solutions engineered from the concepts of digital signal processing and machine-learning. Studies in [2] have shown that automated ophthalmic screening programs alone could save the US healthcare budget nearly 400 million USD per year. Additionally, automated prioritization of eye-care delivery could reduce time delays in treatment by 50%, thereby signi ficantly reducing the chances of acquired blindness [2]. With such
increasing costs of health care, research dedicated towards engineering optimal solutions for ophthalmic image analysis will lead to faster and cheaper diagnostic systems and cost-effective treatment delivery. An example of an existing opthalmic fundus imaging system is shown in Fig. 1.
opthalmic fundus imaging
