Real-time diabetic retinopathy patient screening using multiscale AM-FM methods

In this paper we present a robust and improved system for diabetic retinopathy (DR) screening. The goal of the system is to automatically screen out digital fundus photographs of diabetic patients who do not present signs of DR. This work is motivated by the large amount of diabetics in the world who do not receive their recommended eye exams, leading to widespread blindness as a complication of diabetes. The system is based on multiscale amplitude-modulation frequency-modulation (AM-FM) methods for feature extraction, and uses supervised and unsupervised methods to produce its final output, namely, a normal or abnormal grade. The most time-consuming processing routines of the system are implemented in C using a compute unified device architecture (CUDA) to produce results in real-time. The system was tested using 776 images from 388 patients (one macula-centered image from each eye). During the training phase of the system, the data was divided in 70% for training and 30% for testing. The system was tested using 20 random training/testing distributions, obtaining an average sensitivity of 89% and specificity of 59%. Analysis of sight-threatening conditions resulted in a sensitivity of 98% for these types of cases.