In this paper, we give a review on automatic image processing tools to recognize diseases causing specific distortions in the human retina. After a brief summary of the biology of the retina, we give an overview of the types of lesions that may appear as biomarkers of both eye and non-eye diseases. We present several state-of-the-art procedures to extract the anatomic components and lesions in color fundus photographs and decision support methods to help clinical diagnosis. We list publicly available databases and appropriate measurement techniques to compare quantitatively the performance of these approaches. Furthermore, we discuss on how the performance of image processing-based systems can be improved by fusing the output of individual detector algorithms. Retinal image analysis using mobile phones is also addressed as an expected future trend in this field.
Keywords: ACC, accuracy; AMD, age-related macular degeneration; AUC, area under the receiver operator characteristics curve; Biomedical imaging; Clinical decision support; DR, diabetic retinopathy; FN, false negative; FOV, field-of-view; FP, false positive; FPI, false positive per image; Fundus image analysis; MA, microaneurysm; NA, not available; OC, optic cup; OD, optic disc; PPV, positive predictive value (precision); ROC, Retinopathy Online Challenge; RS, Retinopathy Online Challenge score; Retinal diseases; SCC, Spearman's rank correlation coefficient; SE, sensitivity; SP, specificity; TN, true negative; TP, true positive; kNN, k-nearest neighbor.