Optic disc (OD) and fovea locations are two important anatomical landmarks in automated analysis of retinal disease in
color fundus photographs. This paper presents a new, fast, fully automatic optic disc and fovea localization algorithm
developed for diabetic retinopathy (DR) screening. The optic disc localization methodology comprises of two steps.
First, the OD location is identified using template matching and directional matched filter. To reduce false positives due
to bright areas of pathology, we exploit vessel characteristics inside the optic disc. The location of the fovea is estimated
as the point of lowest matched filter response within a search area determined by the optic disc location. Second, optic
disc segmentation is performed. Based on the detected optic disc location, a fast hybrid level-set algorithm which
combines the region information and edge gradient to drive the curve evolution is used to segment the optic disc
boundary. Extensive evaluation was performed on 1200 images (Messidor) composed of 540 images of healthy retinas,
431 images with DR but no risk of macular edema (ME), and 229 images with DR and risk of ME. The OD location
methodology obtained 98.3% success rate, while fovea location achieved 95% success rate. The average mean absolute
distance (MAD) between the OD segmentation algorithm and "gold standard" is 10.5% of estimated OD radius.
Qualitatively, 97% of the images achieved Excellent to Fair performance for OD segmentation. The segmentation
algorithm performs well even on blurred images.