21 March 2016 Automated detection of retinal landmarks for the identification of clinically relevant regions in fundus photography
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Abstract
Automatic, quick and reliable identification of retinal landmarks from fundus photography is key for measurements used in research, diagnosis, screening and treating of common diseases affecting the eyes. This study presents a fast method for the detection of the centre of mass of the vascular arcades, optic nerve head (ONH) and fovea, used in the definition of five clinically relevant areas in use for screening programmes for diabetic retinopathy (DR). Thirty-eight fundus photographs showing 7203 DR lesions were analysed to find the landmarks manually by two retina-experts and automatically by the proposed method. The automatic identification of the ONH and fovea were performed using template matching based on normalised cross correlation. The centre of mass of the arcades was obtained by fitting an ellipse on sample coordinates of the main vessels. The coordinates were obtained by processing the image with hessian filtering followed by shape analyses and finally sampling the results. The regions obtained manually and automatically were used to count the retinal lesions falling within, and to evaluate the method. 92.7% of the lesions were falling within the same regions based on the landmarks selected by the two experts. 91.7% and 89.0% were counted in the same areas identified by the method and the first and second expert respectively. The inter-repeatability of the proposed method and the experts is comparable, while the 100% intra-repeatability makes the algorithm a valuable tool in tasks like analyses in real-time, of large datasets and of intra-patient variability.
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Giovanni Ometto, Giovanni Ometto, Francesco Calivá, Francesco Calivá, Bashir Al-Diri, Bashir Al-Diri, Toke Bek, Toke Bek, Andrew Hunter, Andrew Hunter, } "Automated detection of retinal landmarks for the identification of clinically relevant regions in fundus photography", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 978429 (21 March 2016); doi: 10.1117/12.2216488; https://doi.org/10.1117/12.2216488
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