Measurement of optical cup-to-disc ratio in fundus images for glaucoma screening

Glaucoma is the second leading cause of blindness. There is no cure for glaucoma yet and early detection is critical to avoid total loss of vision. Cup to disc ratio (CDR) is commonly used by ophthalmologists to diagnose glaucoma. The purpose of this work is to develop an automatic approach to measure the cup to disc ratio (CDR) for glaucoma screening. In this paper, superpixels clustering algorithm; simple linear iterative clustering (SLIC) and a feed-forward neural network classifier have been utilized. A set of superpixels features are extracted and then used for training the classifier. To detect the optic disc and cup boundaries, the classifier is used to classify the superpixels in the region of interest. Then morphological operations and elliptical estimation approach are applied for the final cup and disc boundary detection and segmentation. The CDR is calculated based on these segmentations. Experiments show that by training the non-linear classifier on a set of efficient features, the optic disc and cup can be correctly estimated even in a low contrast images. The results have also shown that effectiveness of the approach with 92% sensitivity and 88% specificity.