Brain-inspired algorithms for retinal image analysis

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, Johannes Schouten, Mengmeng Tong, Hanhui Wu, Han W. van Triest, Shanshan Zhu, Dali Chen, Wei He, Ling XuPing Han, Yan Kang

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


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.
Original languageEnglish
Pages (from-to)1117-1135
JournalMachine Vision and Applications
Issue number8
Publication statusPublished - Nov 2016


  • Retina
  • Diabetic retinopathy
  • Multi-orientation
  • Orientation scores
  • Vessel analysis
  • Tortuosity
  • SE(2)
  • Screening

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