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International Joint Conference on Computer Vision, Imaging and Computer Graphics

VISIGRAPP 2017: Computer Vision, Imaging and Computer Graphics – Theory and Applications pp 262–278Cite as

Automatic Retinal Vascularity Identification and Artery/Vein Classification Using Near-Infrared Reflectance Retinographies

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 983))

Abstract

The retinal microcirculation structure is commonly used as an important source of information in many medical specialities for the diagnosis of relevant diseases such as, for reference, hypertension, arteriosclerosis, or diabetes. Also, the evaluation of the cerebrovascular and cardiovascular disease progression could be performed through the identification of abnormal signs in the retinal vasculature architecture. Given that these alterations affect differently the artery and vein vascularities, a precise characterization of both blood vessel types is also crucial for the diagnosis and treatment of a significant variety of retinal and systemic pathologies. In this work, we present a fully automatic method for the retinal vessel identification and classification in arteries and veins using Optical Coherence Tomography scans. In our analysis, we used a dataset composed by 30 near-infrared reflectance retinography images from different patients, which were used to test and validate the proposed method. In particular, a total of 597 vessel segments were manually labelled by an expert clinician, being used as groundtruth for the validation process. As result, this methodology achieved a satisfactory performance in the complex issue of the retinal vessel tree identification and classification.

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Acknowledgments

This work is supported by the Instituto de Salud Carlos III, Government of Spain and FEDER funds of the European Union through the PI14/02161 and the DTS15/00153 research projects and by the Ministerio de Economía y Competitividad, Government of Spain through the DPI2015-69948-R research project. Also, this work has received financial support from the European Union (European Regional Development Fund - ERDF) and the Xunta de Galicia, Centro singular de investigación de Galicia accreditation 2016-2019, Ref. ED431G/01; and Grupos de Referencia Competitiva, Ref. ED431C 2016-047.

Author information

Authors and Affiliations

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

    Joaquim de Moura, Jorge Novo, Marcos Ortega & Noelia Barreira

  2. CITIC - Research Center of Information and Communication Technologies, University of A Coruña, A Coruña, Spain

    Joaquim de Moura, Jorge Novo, Marcos Ortega & Noelia Barreira

  3. Instituto Oftalmológico Victoria de Rojas, A Coruña, Spain

    Pablo Charlón

Authors
  1. Joaquim de Moura
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  2. Jorge Novo
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  3. Marcos Ortega
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  4. Noelia Barreira
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  5. Pablo Charlón
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Corresponding author

Correspondence to Joaquim de Moura .

Editor information

Editors and Affiliations

  1. University of Lisbon, Lisbon, Portugal

    Ana Paula Cláudio

  2. University of Strasbourg, Strasbourg, France

    Dominique Bechmann

  3. University of Angers, Angers, France

    Paul Richard

  4. Tokyo University of Science, Tokyo, Japan

    Takehiko Yamaguchi

  5. University of Münster, Münster, Nordrhein-Westfalen, Germany

    Lars Linsen

  6. University of Groningen, Groningen, The Netherlands

    Alexandru Telea

  7. Apple Inc., Cupertino, CA, USA

    Francisco Imai

  8. Jean Monnet University, Saint-Etienne, France

    Alain Tremeau

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de Moura, J., Novo, J., Ortega, M., Barreira, N., Charlón, P. (2019). Automatic Retinal Vascularity Identification and Artery/Vein Classification Using Near-Infrared Reflectance Retinographies. In: Cláudio, A., et al. Computer Vision, Imaging and Computer Graphics – Theory and Applications. VISIGRAPP 2017. Communications in Computer and Information Science, vol 983. Springer, Cham. https://doi.org/10.1007/978-3-030-12209-6_13

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  • DOI: https://doi.org/10.1007/978-3-030-12209-6_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-12208-9

  • Online ISBN: 978-3-030-12209-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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