A unified approach for detection of diagnostically significant regions-of-interest in retinal fundus images

Automatic detection of optic disc and fovea is a precursor to the computer-aided analysis of retinal pathologies. In this paper, we present a unified approach for optic disc and fovea detection based on normalized cross-correlation technique. The algorithm performance is optimized by introducing vector inner products and norms instead of conventional mean and variance computations. We report optic disc detection results on four publicly available fundus image databases amounting to a total of 1451 fundus images and fovea detection results on another four publicly available fundus image databases amounting to a total of 1454 fundus images. The proposed method results in an optic disc detection accuracy of 99.01%, 95.67%, 99.09%, and 100% on DRISHTI-GS, MESSIDOR, DRIONS-DB, and DRIVE fundus image databases, respectively, and fovea detection accuracy of 94.83%, 84.62%, 95.51%, and 97.14% on MESSIDOR, DIARETDB0, DIARETDB1, and DRIVE fundus image databases, respectively. The speed of optic disc and fovea detection has been improved considerably by downsampling technique. In addition, we report the effect of downsampling on the detection accuracy.