Feature Extraction and Selection for the Automatic Detection of Hard Exudates in Retinal Images

Abstract

Diabetic Retinopathy (DR) is a common cause of visual impairment among people of working age in industrialized countries. Automatic recognition of DR lesions, like hard exudates (HEs), in fundus images can contribute to the diagnosis and screening of this disease. In this study, we extracted a set of features from image regions and selected the subset which best discriminates between HEs and the retinal background. The selected features were then used as inputs to a multilayer perceptron (MLP) classifier to obtain a final segmentation of HEs in the image. Our database was composed of 100 images with variable color, brightness, and quality. 50 of them were used to train the MLP classifier and the remaining 50 to assess the performance of the method. Using a lesion-based criterion, we achieved a mean sensitivity of 84.4% and a mean positive predictive value of 62.7%. With an image-based criterion, our approach reached a 100% mean sensitivity, 84.0% mean specificity and 92.0% mean accuracy.