Automatic Image Processing Algorithm to Detect Hard Exudates based on Mixture Models

Automatic detection of hard exudates from retinal images is clinically significant. Hard exudates are associated with diabetic retinopathy and have been found to be one of the most prevalent earliest clinical signs of retinopathy. In this study, an automatic method to detect hard exudates is proposed. The algorithm is based on mixture models to dynamically threshold the images in order to separate hard exudates from background. We prospectively assessed the algorithm performance using a database of 20 retinal images with variable color, brightness, and quality. The algorithm obtained a sensitivity of 90.23% and a predictive value of 82.5% using a lesion-based criterion. The image-based classification accuracy is also evaluated obtaining a sensitivity of 100% and a specificity of 90%