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4 CONCLUSIONS
An automated method for A/V classification in
retinal vasculature based on colour features utilizing
the ensemble classifer of boot strapped decision
trees is presented. To the best of our knowledge, this
is the first time the decision trees based ensemble
classifier has been used for A/V classification.
An application of image processing algorithms
for computer assisted analysis of digital fundus
images offers a number of advantages over a manual
system, including fast, timely and reliable
quantification of abnormalities. The presented
methodology will be incorporated in to a software
package QUARTZ (QUantitative Analysis of
Retinal vessel Topology and siZe). The QUARTZ
software will assist in examining arterio-venous
morphological associations with cardiovascular risk
factors and outcomes in large population based
studies, furthering our understanding of the vascular
changes / consequences associated with the
development of disease.
In future we aim to extend the QUARTZ
software to incorporate the analysis of other retinal
vessel features pathognomonic of cardiovascular
disease, including measurement of arterio-venous
ratio, identification of venous beading and
quantification of arterio-venous nicking.
ACKNOWLEDGEMENTS
The authors would like to thank Professor Paul
Foster and the European Investigation into Cancer in
Norfolk (EPIC Norfolk) study for providing the
retinal images used in this analysis. The EPIC
Norfolk study is supported by grants from the
Medical Research Council, Cancer Research UK and
Research into Ageing.
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