Brain-inspired algorithms for retinal image analysis

Beitrag in einer Fachzeitschrift
(Originalarbeit)


Details zur Publikation

Autorinnen und Autoren: Ter Haar Romeny BM, Bekkers EJ, Zhang J, Abbasi-Sureshjani S, Huang F, Duits R, Dashtbozorg B, Berendschot TTJM, Smit-Ockeloen I, Eppenhof KAJ, Feng J, Hannink J, Schouten J, Tong M, Wu H, van Triest HW, Zhu S, Chen D, He W, Xu L, Han P, Kang Y
Zeitschrift: Machine Vision and Applications
Jahr der Veröffentlichung: 2016
Seitenbereich: 1-19
ISSN: 1432-1769
eISSN: 0932-8092


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.



FAU-Autorinnen und Autoren / FAU-Herausgeberinnen und Herausgeber

Hannink, Julius
Stiftungs-Juniorprofessur für Sportinformatik (Digital Sports)


Einrichtungen weiterer Autorinnen und Autoren

China Medical University (CMU) / 中国医科大学
Eindhoven University of Technology / Technische Universiteit Eindhoven (TU/e)
Northeastern University / 东北大学
Shenyang He Eye Hospital
Universiteitskliniek voor Oogheelkunde / University Clinic for Ophthalmology


Zitierweisen

APA:
Ter Haar Romeny, B.M., Bekkers, E.J., 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.

BibTeX: 

Zuletzt aktualisiert 2019-06-08 um 09:03