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14 March 2011 Simultaneous automatic detection of optic disc and fovea on fundus photographs
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Proceedings Volume 7962, Medical Imaging 2011: Image Processing; 79622T (2011) https://doi.org/10.1117/12.877801
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States
Abstract
We describe an automated and simultaneous localization method for the optic disc and the fovea for fundus photographs. The method is enhanced by a correction step, which allows the detection result of one structure to facilitate the detection of the other one. In the first step of the method, a set of features is extracted from the color fundus image, and the relationship between the features and a distance variable is established during the training phase. For a test image, the same set of features is measured and the distance to the optic disc and the fovea can be estimated using k-nearest-neighbor classification. A probability image with every pixel labeled a probability of within the optic disc or the fovea is generated. In the second step of the method, a second k-nearest-neighbor classification is applied on the probability image. Another set of features is extracted and trained. For a test image, detected high likelihood regions from the first step can be enhanced only if they satisfy the trained relationship. A set of 250 color fundus images from the left eye were used to train the system. Another set of 310 color fundus images were used to test the system. The correct rate for the optic disc is 93.9%. The correct rate for the fovea is 88.1%. This is a fully automatic method to detect the optic disc and fovea simultaneously with excellent performance. We are currently expanding validation on larger datasets.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xiayu Xu, Mona K. Garvin, Michael D. Abràmoff, and Joseph M. Reinhardt "Simultaneous automatic detection of optic disc and fovea on fundus photographs", Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79622T (14 March 2011); https://doi.org/10.1117/12.877801
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