Coupled sparse dictionary for depth-based cup segmentation from single color fundus image.

We present a novel framework for depth based optic cup boundary extraction from a single 2D color fundus photograph per eye. Multiple depth estimates from shading, color and texture gradients in the image are correlated with Optical Coherence Tomography (OCT) based depth using a coupled sparse dictionary, trained on image-depth pairs. Finally, a Markov Random Field is formulated on the depth map to model the relative depth and discontinuity at the cup boundary. Leave-one-out validation of depth estimation on the INSPIRE dataset gave average correlation coefficient of 0.80. Our cup segmentation outperforms several state-of-the-art methods on the DRISHTI-GS dataset with an average F-score of 0.81 and boundary-error of 21.21 pixels on test set against manual expert markings. Evaluation on an additional set of 28 images against OCT scanner provided groundtruth showed an average rms error of 0.11 on Cup-Disk diameter and 0.19 on Cup-disk area ratios.