Texture Analysis for Glaucoma Classification

Abstract

In this paper, we present our ongoing work on glaucoma classification using fundus images. The approach makes use of texture analysis based on Binary Robust Independent Elementary Features (BRIEF). This texture measurement is chosen because it can address the illumination issues of the retinal images and has a lower degree of computational complexity than most of the existing texture measurement methods currently used in the literature. Contrary to other approaches, the texture measures are extracted from the whole retina image without targeting any specific region. The method was tested on a set of 196 images composed of 110 healthy retina images and 86 glaucomatous images and achieved an area under curve (AUC) of 84%. A comparison performance with other texture measurements is also included, which shows our method to be superior.