Brain-inspired algorithms for retinal image analysis

B.M. ter Haar Romeny, E.J. Bekkers, J. Zhang, S. Abbasi-Sureshjani, F. Huang, R. Duits, Behdad Dasht Bozorg, T.T.J.M. Berendschot, I. Smit-Ockeloen, K.A.J. Eppenhof, J. Feng, J. Hannink, J. Schouten, M. Tong, H. Wu, J.W. van Triest, S. Zhu, D. Chen, W. He, L. XuP. Han, Y. Kang

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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.

Originele taal-2Engels
Pagina's (van-tot)1117–1135
Aantal pagina's19
TijdschriftMachine Vision and Applications
Nummer van het tijdschrift8
StatusGepubliceerd - nov 2016

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    ter Haar Romeny, B. M., Bekkers, E. J., Zhang, J., Abbasi-Sureshjani, S., Huang, F., Duits, R., Dasht Bozorg, B., Berendschot, T. T. J. M., Smit-Ockeloen, I., Eppenhof, K. A. J., Feng, J., Hannink, J., Schouten, J., Tong, M., Wu, H., van Triest, J. W., Zhu, S., Chen, D., He, W., ... Kang, Y. (2016). Brain-inspired algorithms for retinal image analysis. Machine Vision and Applications, 27(8), 1117–1135.