Robust Object Extraction and Change Detection in Retinal Images for Diabetic Clinical Studies

With the rapid advances in computing and electronic imaging technology, there has been increasing interest in developing computer aided medical diagnosis systems to improve the medical service for the public. Images of ocular fundus provide crucial observable features for diagnosing many kinds of pathologies such as diabetes, hypertension, and arteriosclerosis. A computer-aided retinal image analysis system can help eye specialists to screen larger populations and produce better evaluation of treatment and more effective clinical study. This paper is focused on the immediate needs for clinical studies on diabetic patients. Our system includes multiple feature extraction, robust retinal vessel segmentation, hierarchical change detection and classification. The output throughout this system will assist doctors to speed up screening large populations for abnormal cases, and facilitate evaluation of treatment for clinical study