Automatic localization of the macula in a supervised graph-based approach with contextual superpixel features

Localization of the macula centre is an important step in retinal image analysis, in particular for macular disease. We propose the use of a superpixel-based approach for macular localization. Features are extracted from the superpixels, including a proposed feature which aims to describe the extent of the local region due to the superpixel influence. These features are used to calculate probability estimates to determine the macula centre. We evaluated our results on a large dataset of 728 images comprising of normal, glaucoma and AMD eyes. The results are promising. Our method achieved an average error of 30pixels, with all the detected macula centres within 1/8 disc diameters of the reference ground truth, which is lower than the other methods tested.