Automatic detection and characterisation of retinal vessel tree bifurcations and crossovers in eye fundus images

Authors:
David Calvo;Marcos Ortega;Manuel G. Penedo;Jose Rouco
Affiliations:
Dept. of Computer Science, University of A Coruña, Spain;Dept. of Computer Science, University of A Coruña, Spain;Dept. of Computer Science, University of A Coruña, Spain;Dept. of Computer Science, University of A Coruña, Spain
Venue:
Computer Methods and Programs in Biomedicine
Year:
2011

Citing 5
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Automatic Detection of Vascular Bifurcations and Crossovers from Color Retinal Fundus Images

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Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms

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Model-based method for improving the accuracy and repeatability of estimating vascular bifurcations and crossovers from retinal fundus images

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Automatic detection of vascular bifurcations in segmented retinal images using trainable COSFIRE filters

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Automatic vessel network features quantification using local vessel pattern operator

Computers in Biology and Medicine

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Abstract

Analysis of retinal vessel tree characteristics is an important task in medical diagnosis, specially in cases of diseases like vessel occlusion, hypertension or diabetes. The detection and classification of feature points in the arteriovenous eye tree will increase the information about the structure allowing its use for medical diagnosis. In this work a method for detection and classification of retinal vessel tree feature points is presented. The method applies and combines imaging techniques such as filters or morphologic operations to obtain an adequate structure for the detection. Classification is performed by analysing the feature points environment. Detection and classification of feature points is validated using the VARIA database. Experimental results are compared to previous approaches showing a much higher specificity in the characterisation of feature points while slightly increasing the sensitivity. These results provide a more reliable methodology for retinal structure analysis.