Detection of Exudates and Optic Disc Using Superpixel Multifeature Classification

Automated optic disk (OD) detection plays an important role in developing a computer aided system for eye diseases. A classifier model is trained based on structured learning. Thresholding is performed on the edge map thus a binary image of the OD is obtained. The results (an area overlap and Dices coefficients of 0.8605 and 0.9181, respectively, an accuracy of 0.9777, and a true positive and false positive fraction of 0.9183 and 0.0102) show that this method is very competitive with the state-of-the-art methods and is a reliable tool for the segmentation of OD. Exudates can be regarded as one of the most prevalent clinical signs of diabetic retinopathy, and the detection of exudates has important clinical significance in diabetic retinopathy diagnosis. A novel approach named superpixel multi-feature classification for the automatic detection of exudates is developed. First, an entire image is segmented into a series of superpixels considered as candidates. A supervised multi-variable classification algorithm is also introduced to distinguish the true exudates from the spurious candidates. Finally, a novel optic disc detection technique is designed to further improve the performance of classification accuracy. Extensive experiments are carried out on two publicly available online databases, DiaretDB1, and e-ophtha EX.