Automatic detection of red lesions in retinal images using a multilayer perceptron neural network

Diabetic Retinopathy (DR) is an important cause of visual impairment among people of working age in industrialized countries. Automatic detection of DR clinical signs in retinal images would be an important contribution to the diagnosis and screening of the disease. The aim of the present study is to automatically detect some of these clinical signs: red lesions (RLs), like hemorrhages (HEs) and microaneurysms (MAs). Based on their properties, we extracted a set of features from image regions and selected the subset which best discriminated between these RLs and the retinal background. A multilayer perceptron (MLP) classifier was subsequently used to obtain the final segmentation of RLs. Our database was composed of 100 images with variable color, brightness, and quality. 50 of them were used to obtain the examples to train the MLP classifier. The remaining 50 images were used to test the performance of the method. Using a lesion based criterion, we reached a mean sensitivity of 86.1% and a mean positive predictive value of 71.4%. With an image-based criterion, we achieved a 100% mean sensitivity, 60.0% mean specificity and 80.0% mean accuracy.