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Automatic Arteriovenous Nicking Identification by Color Fundus Images Analysis

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Image Analysis and Recognition (ICIAR 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8815))

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Abstract

Retinal arteriovenous nicking (AVN) assessment has been considered a very important indicator of cardiovascular and cerebrovascular diseases. A computerized method to infer the AVN presence in retinal images could increase the reproducibility and accuracy of this analysis that for now has been done by ophthalmologists in a subjective and qualitative manner. Therefore, a new approach is proposed for the AVN assessment in color fundus images. First the algorithm segments the blood vessels by means of a multi-scale line detector. The arteriovenous cross points are then detected and classified as AVN presence or absence with an SVM. The proposed approach is clearly efficient in separating normal cases from the evident or severe AVN cases.

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Author information

Authors and Affiliations

  1. Enermeter, Parque Industrial Celeirós, Lugar de Gaião, 4705-025, Braga, Portugal

    Carla Pereira, Diana Veiga & Manuel Ferreira

  2. Centro Algoritmi, Campus Azurém, 4800-058, Guimarães, Portugal

    Diana Veiga & Manuel Ferreira

  3. Oftalmocenter, Azurém, 4800-045, Guimarães, Portugal

    Luís Gonçalves

Authors
  1. Carla Pereira
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  2. Diana Veiga
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  3. Luís Gonçalves
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  4. Manuel Ferreira
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Corresponding author

Correspondence to Carla Pereira .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Porto, Porto, Portugal

    Aurélio Campilho

  2. Dept. of Electrical and Computer Eng., University of Waterloo, Waterloo, Ontario, Canada

    Mohamed Kamel

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© 2014 Springer International Publishing Switzerland

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Pereira, C., Veiga, D., Gonçalves, L., Ferreira, M. (2014). Automatic Arteriovenous Nicking Identification by Color Fundus Images Analysis. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2014. Lecture Notes in Computer Science(), vol 8815. Springer, Cham. https://doi.org/10.1007/978-3-319-11755-3_36

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  • DOI: https://doi.org/10.1007/978-3-319-11755-3_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11754-6

  • Online ISBN: 978-3-319-11755-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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