Recurrent neural network based retinal nerve fiber layer defect detection in early glaucoma

Abstract

Retinal nerve fiber layer defect (RNFLD) is the earliest objective evidence of glaucoma in fundus images. Glaucoma is an optic neuropathy which causes irreversible vision impairment. Early glaucoma detection and its prevention are the only way to prevent further damage to human vision. In this paper, we propose a new automated method for RNFLD detection in fundus images through patch features driven recurrent neural network (RNN). A new dataset of fundus images is created for evaluation purpose which contains several challenging RNFLD boundaries. The true boundary pixels are classified using the RNN trained by novel cumulative zero count local binary pattern (CZC-LBP), directional differential energy (DDE) patch features. The experimental results demonstrate high RNFLD detection rate along with accurate boundary localization.