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

Research output: Contribution to journalArticleAcademicpeer-review

20 Citations (Scopus)
501 Downloads (Pure)

Abstract

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
Number of pages19
JournalMachine Vision and Applications
Volume27
Issue number8
DOIs
Publication statusPublished - Nov 2016

Keywords

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

Fingerprint Dive into the research topics of 'Brain-inspired algorithms for retinal image analysis'. Together they form a unique fingerprint.

Cite this