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Authors: M. M. Fraz 1 ; A. R. Rudincka 2 ; C. G. Owen 2 ; D. P. Strachan 2 and S. A. Barman 1

Affiliations: 1 Kingston University London, United Kingdom ; 2 University of London, United Kingdom

Keyword(s): Medical Image Analysis, Retinal Image Processing, Artery Vein Classification, Ensemble Learning.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Color and Texture Analyses ; Computer Vision, Visualization and Computer Graphics ; Features Extraction ; Image and Video Analysis ; Medical Image Applications ; Segmentation and Grouping

Abstract: The shape and size of retinal vessels have been prospectively associated with cardiovascular outcomes in adult life, and with cardiovascular precursors in early life, suggesting life course patterning of vascular development. However, the shape and size of arterioles and venules may show similar or opposing associations with disease precursors / outcomes. Hence accurate detection of vessel type is important when considering cardio-metabolic influences on vascular health. This paper presents an automated method of identifying arterioles and venules, based on colour features using the ensemble classifier of boot strapped decision trees. The classifier utilizes pixel based features, vessel profile based features and vessel segment based features from both RGB and HIS colour spaces. To the best of our knowledge, the decision trees based ensemble classifier has been used for the first time for arteriole/venule classification. The classification is performed across the entire image, incl uding the optic disc. The methodology is evaluated on 3149 vessel segments from 40 colour fundus images acquired from an adult population based study in the UK (EPIC Norfolk), resulting in 83% detection rate. This methodology can be further developed into an automated system for measurement of arterio-venous ratio and quantification of arterio-venous nicking in retinal images, which may be of use in identifying those at high risk of cardiovascular events, in need of early intervention. (More)

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Paper citation in several formats:
M. Fraz, M.; R. Rudincka, A.; G. Owen, C.; P. Strachan, D. and A. Barman, S. (2014). Automated Arteriole and Venule Recognition in Retinal Images using Ensemble Classification. In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-009-3; ISSN 2184-4321, pages 194-202. DOI: 10.5220/0004733701940202

@conference{visapp14,
author={M. {M. Fraz}. and A. {R. Rudincka}. and C. {G. Owen}. and D. {P. Strachan}. and S. {A. Barman}.},
title={Automated Arteriole and Venule Recognition in Retinal Images using Ensemble Classification},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={194-202},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004733701940202},
isbn={978-989-758-009-3},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014)
TI - Automated Arteriole and Venule Recognition in Retinal Images using Ensemble Classification
SN - 978-989-758-009-3
IS - 2184-4321
AU - M. Fraz, M.
AU - R. Rudincka, A.
AU - G. Owen, C.
AU - P. Strachan, D.
AU - A. Barman, S.
PY - 2014
SP - 194
EP - 202
DO - 10.5220/0004733701940202

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