n 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). Dur...

This paper presents a multiscale method to detect neovascularization in the optic disc (NVD) using fundus images. Our method is applied to a manually selected region of interest (ROI) containing the optic disc. All the vessels in the ROI are segmented by adaptively combining contrast enhancement methods with a vessel segmentation technique. Textural features extracted using multiscale amplitude-modulation frequency-modulation, morphological granulometry, and fractal dimension are used. A linear SVM is used to perform the classification, which is tested by means of 10-fold cross-validation. The performance is evaluated using 300 images achieving an AUC of 0.93 with maximum accuracy of 88%.