The invention relates to methods and systems for automatically detecting an optic disc in a retinal fundus autofluorescence image. A monochromatic image of a retina is obtained by stimulated autofluorescence. The image is processed using filters and machine learning to identify blood vessel segments and to identify pixels as possible optic disc pixels. The possible optic disc pixels are grouped into clusters, and a best fit circle (or portion thereof lying within the image) is fitted to each cluster. The circle may be enlarged to improve image contrast at the circumference of the circle. Blood vessel segments that intersect the circle are identified, and metrics are derived from such intersecting blood vessel segments. These metrics are assessed by machine learning processes to determine the probability that each cluster contains the optic disc, and the contours of the optic disc are further determined by analysis of the possible optic disc pixels.