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

  • Joaquim de MouraEmail author
  • Jorge Novo
  • Marcos Ortega
  • Noelia Barreira
  • Pablo Charlón
Conference paper
Part of the Communications in Computer and Information Science book series (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.

Keywords

Retinal imaging Vascular tree Segmentation Artery/vein classification 

Notes

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.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Joaquim de Moura
    • 1
    • 2
    Email author
  • Jorge Novo
    • 1
    • 2
  • Marcos Ortega
    • 1
    • 2
  • Noelia Barreira
    • 1
    • 2
  • Pablo Charlón
    • 3
  1. 1.Department of Computer ScienceUniversity of A CoruñaA CoruñaSpain
  2. 2.CITIC - Research Center of Information and Communication TechnologiesUniversity of A CoruñaA CoruñaSpain
  3. 3.Instituto Oftalmológico Victoria de RojasA CoruñaSpain

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