Conference Proceeding
Authors: Behdad Dashtbozorg (Eindhoven University of Technology, Eindhoven, the Netherlands) , Samaneh Abbasi-Sureshjani (Eindhoven University of Technology, Eindhoven, the Netherlands) , Jiong Zhang (Eindhoven University of Technology, Eindhoven, the Netherlands) , Fan Huang (Eindhoven University of Technology, Eindhoven, the Netherlands) , Erik Bekkers (Eindhoven University of Technology, Eindhoven, the Netherlands) , Bart ter Haar Romeny (Northeastern University, Shenyang, China)
This paper introduces a retinal image analysis infrastructure for the automatic assessment of biomarkers related to early signs of diabetes, hypertension and other systemic diseases. The developed application provides several tools, namely normalization, vessel enhancement and segmentation, optic disc and fovea detection, junction detection, bifurcation/crossing discrimination, artery/vein classification and red lesion detection. The pipeline of these methods allows the assessment of important biomarkers characterizing dynamic properties of retinal vessels, such as tortuosity, width, fractal dimension and bifurcation geometry features.
Keywords: Retina, biomarkers, diabetes, orientation scores, curvature, vessel segmentation, artery/vein classification, bifurcation, optic disc, fovea, microaneurysm, fractal dimension
How to Cite: Dashtbozorg, B. , Abbasi-Sureshjani, S. , Zhang, J. , Huang, F. , Bekkers, E. & Haar Romeny, B. t. (2016) “Infrastructure for Retinal Image Analysis”, Proceedings of the Ophthalmic Medical Image Analysis International Workshop. 3(2016). doi: https://doi.org/10.17077/omia.1053