Retinal image analysis is a challenging problem due to the precise quantification required and the huge numbers of images produced in screening programs. This paper describes a series of innovative brain-inspired algorithms for automated retinal image analysis, recently developed for the RetinaCheck project, a large-scale screening program for diabetic retinopathy and other retinal diseases in Northeast China. The paper discusses the theory of orientation scores, inspired by cortical multi-orientation pinwheel structures, and presents applications for automated quality assessment, optic nerve head detection, crossing-preserving enhancement and segmentation of retinal vasculature, arterio-venous ratio, fractal dimension, and vessel tortuosity and bifurcations. Many of these algorithms outperform state-of-the-art techniques. The methods are currently validated in collaborating hospitals, with a rich accompanying base of metadata, to phenotype and validate the quantitative algorithms for optimal classification power.
['Bart M. ter Haar Romeny', 'Erik J. Bekkers', 'Jiong Zhang', 'Samaneh Abbasi-Sureshjani', 'Fan Huang', 'Remco Duits', 'Behdad Dashtbozorg', 'Tos T. J. M. Berendschot', 'Iris Smit-Ockeloen', 'Koen A.J. Eppenhof', 'Jinghan Feng', 'Julius Hannink', 'Jan S. A. G. Schouten', 'Mengmeng Tong', 'Hanhui Wu', 'Han J. W. van Triest', 'Shanshan Zhu', 'Dali Chen', 'Wei He', 'Ling Xu', 'P. Han', 'Yan Kang']
Brain-inspired algorithms for retinal image analysis