Automatic macula detection from retinal images by a line operator
This paper presents an automatic macula detection technique that makes use of the circular brightness profile of the macula: the macula is usually darker than the surrounding pixels whose intensities increase gradually with their distances from the macula center. A line operator is designed to capture the macula circular brightness profile, which evaluates the image brightness variation along multiple line segments of specific orientations that pass through each retinal image pixel. The orientation of the line segment with the minimum/ maximum variation has specific patterns that indicate the position of the macula efficiently. The proposed technique has been tested over DRIVE project's dataset and the STARE project's dataset. Experiments show that the accuracies reach up to 100% and 95.45%, respectively, based on 35 and 44 retinal images having discernible macula within the two public datasets.