Detecting Wedge Shaped Defects in Polarimetric Images of the Retinal Nerve Fiber Layer

Abstract

Wedge shaped defects of the retinal nerve fiber layer (RNFL) may occur in glaucoma. Currently, automatic detection of wedge shaped defects in Scanning Laser Polarimetry images of the retinal nerve fiber layer is unavailable; an automatic classification is currently based only on global parameters, thereby ignoring important local information. Our method works by a modified dynamic programming technique that searches for locally strong edges with a preference for straight edges. These edges are initially classified based on their strength and then combined into wedge shaped defects. The results of our method on a limited set of 45 images yields a sensitivity of 88% and a specificity of 92%. More importantly, it shows that it is possible to automatically extract local RNFL defects such as wedges.