Brain-inspired algorithms for retinal image analysis
Author(s): Ter Haar Romeny B, Bekkers E, Zhang J, Abbasi-Sureshjani S, Huang F, Duits R, Dashtbozorg B, Berendschot T, Smit-Ockeloen I, Eppenhof K, Feng J, Hannink J, Schouten J, Tong M, Wu H, van Triest H, Zhu S, Chen D, He W, Xu L, Han P, Kang Y
Publication year: 2016
Pages range: 1-19
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.
FAU Authors / FAU Editors How to cite
APA: Ter Haar Romeny, B., Bekkers, E., Zhang, J., Abbasi-Sureshjani, S., Huang, F., Duits, R.,... Kang, Y. (2016). Brain-inspired algorithms for retinal image analysis. Machine Vision and Applications, 1-19. https://dx.doi.org/10.1007/s00138-016-0771-9
MLA: Ter Haar Romeny, Bart M., et al. "Brain-inspired algorithms for retinal image analysis." Machine Vision and Applications (2016): 1-19.