Fovea localization in retinal images using spatial color histograms

The automatic location of the fovea is very useful for diagnosing retinal diseases. It is a complex problem for which different solutions have been proposed based on classical image processing and Deep Learning techniques. The method presented in this paper is based on histograms that combine spatial and color information in such a way that the spatial coordinates are incorporated into conventional color histograms as an additional dimension. The binarization of these histograms retains a considerable amount of relevant information from the original image, allowing them to be processed as if they were ordinary images. This approach to the problem results in a simple, fast and effective method for locating the fovea. Different experiments have been carried out with three popular sets of images: Messidor, REFUGE1 and DIARETDB1, and a comparison was made with other state-of-the-art techniques. Our results show that the proposed method, despite its simplicity, is capable of surpassing many of these techniques.