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Improvements in retinal vessel clustering techniques: towards the automatic computation of the arterio venous ratio

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Abstract

Retinal blood vessel structure is an important indicator for diagnosis of several diseases such as diabetes, hypertension, arteriosclerosis, or stroke. These pathologies cause early alterations in the blood vessels that affect veins and arteries differently. In this sense, the arterio venous ratio is a measurement that evaluates these alterations and, consequently, the condition of the patient. Thus, a precise identification of both types of vessels is necessary in order to develop an automatic diagnosis system, to quantify the seriousness of disease, or to monitor the therapy. The classification of vessels into veins and arteries is difficult due to the inhomogeneity in the retinal image lightness and the similarity of both structures. In this paper, several image feature sets have been combined with three clustering strategies in order to find a suitable characterization methodology. The best strategy has managed to classify correctly the 86.34% of the vessels improving the results obtained with previous techniques.

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Authors and Affiliations

  1. Department of Computer Science, University of A Coruña, A Coruña, Spain

    S. G. Vázquez, N. Barreira, M. G. Penedo & M. Ortega

  2. Service of Internal Medicine, Hospital de Conxo, Santiago de Compostela, Spain

    A. Pose-Reino

Authors
  1. S. G. Vázquez
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  2. N. Barreira
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  3. M. G. Penedo
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  4. M. Ortega
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  5. A. Pose-Reino
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Correspondence to S. G. Vázquez.

Additional information

Communicated by G. Liotta.

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Vázquez, S.G., Barreira, N., Penedo, M.G. et al. Improvements in retinal vessel clustering techniques: towards the automatic computation of the arterio venous ratio. Computing 90, 197–217 (2010). https://doi.org/10.1007/s00607-010-0114-z

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  • DOI: https://doi.org/10.1007/s00607-010-0114-z

Keywords

Mathematics Subject Classification (2000)

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