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Proceedings Paper

Retinal health information and notification system (RHINO)
Author(s): Behdad Dashtbozorg; Jiong Zhang; Samaneh Abbasi-Sureshjani; Fan Huang; Bart M. ter Haar Romeny
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Paper Abstract

The retinal vasculature is the only part of the blood circulation system that can be observed non-invasively using fundus cameras. Changes in the dynamic properties of retinal blood vessels are associated with many systemic and vascular diseases, such as hypertension, coronary heart disease and diabetes. The assessment of the characteristics of the retinal vascular network provides important information for an early diagnosis and prognosis of many systemic and vascular diseases. The manual analysis of the retinal vessels and measurement of quantitative biomarkers in large-scale screening programs is a tedious task, time-consuming and costly. This paper describes a reliable, automated, and efficient retinal health information and notification system (acronym RHINO) which can extract a wealth of geometric biomarkers in large volumes of fundus images. The fully automated software presented in this paper includes vessel enhancement and segmentation, artery/vein classification, optic disc, fovea, and vessel junction detection, and bifurcation/crossing discrimination. Pipelining these tools allows the assessment of several quantitative vascular biomarkers: width, curvature, bifurcation geometry features and fractal dimension. The brain-inspired algorithms outperform most of the state-of-the-art techniques. Moreover, several annotation tools are implemented in RHINO for the manual labeling of arteries and veins, marking optic disc and fovea, and delineating vessel centerlines. The validation phase is ongoing and the software is currently being used for the analysis of retinal images from the Maastricht study (the Netherlands) which includes over 10,000 subjects (healthy and diabetic) with a broad spectrum of clinical measurements

Paper Details

Date Published: 3 March 2017
PDF: 6 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013437 (3 March 2017); doi: 10.1117/12.2253839
Show Author Affiliations
Behdad Dashtbozorg, Eindhoven Univ. of Technology (Netherlands)
Jiong Zhang, Eindhoven Univ. of Technology (Netherlands)
Samaneh Abbasi-Sureshjani, Eindhoven Univ. of Technology (Netherlands)
Fan Huang, Eindhoven Univ. of Technology (Netherlands)
Bart M. ter Haar Romeny, Northeastern Univ. (China)
Eindhoven Univ. of Technology (Netherlands)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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