Automatic glaucoma assessment from angio-OCT images

A variety of imaging modalities have been used for developing diagnostic aids for glaucoma assessment. Structural imaging modalities such as colour fundus imaging and optical coherence tomography (OCT) have been investigated for automatically estimating key parameters for glaucoma assessment such as cup to disc diameter ratio and thickness of the retinal nerve fibre layer (RNFL). OCT-based angiography or OCTA is a new modality which provides structural and an-giographic information about the retinal layers. We present a method for glaucoma detection using OCTA images. Specifically, the capillary density at various layers and thickness of RNFL are estimated and used to classify a given OCTA volume as glaucamatous or not. RNFL thickness is estimated using polynomial fitting to intensity profiles of OCT slices. The capillary density is estimated from the angioflow images using morphological processing to extract the optic nerve head (ONH) and vessel detection in a region of interest defined around the ONH. A system trained on these two features was evaluated on a dataset of 67 eyes (49 normal and 18 glaucomatous) and found to have a sensitivity of 94.44 % and specificity of 91.67%. This demonstrates the potential of the new modality for glaucoma assessment.