Automated localization of macula-fovea area on retina images using blood vessel network topology

In this paper, we propose a simple yet robust unsupervised algorithm for automated localization of macula-fovea area on retina images. The small sizes and weak contrast of the macula-fovea area on retina images make it unreliable to detect it directly. As such, we extract the retina blood vessel network topology based on local energy function of blood vessel widths and densities and use it as the main image cue to position the macula-fovea area. Regardless of the severity of most retinal diseases as well as variations in field clarity, the high level topology of the retinal blood vessel flows remains fairly predictable. Compared with conventional algorithms, our method can effectively localize the macula-fovea area on retina images with inadequate field clarity and diseased conditions. The algorithm is tested on both STARE and DRIVE retina image databases and gained satisfactory detection results.