Retinal Blood Vessel Segmentation by Multi-channel Deep Convolutional Autoencoder

  • Andrés OrtizEmail author
  • Javier Ramírez
  • Ricardo Cruz-Arándiga
  • María J. García-Tarifa
  • Francisco J. Martínez-Murcia
  • Juan M. Górriz
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 771)


The evaluation and diagnosis of retina pathologies are usually made by the analysis of different image modalities that allows to explore its structure. The most popular retina image method is the retinography, a technique to show the retina and other structures in the fundus of the eye. This paper deals with an important stage of the retina image processing for a diagnosis tool which aims to show the blood vessel structure. Our proposal is based on a deep convolutional neural network, that avoids any preprocessing stage such as gray scale conversion, histogram equalization, and other image transformations that determine the final result. Thus, we obtain the blood vessel segmentation directly from the original RGB color retinography image. The results obtained with our method are comparable to the state-of-the art methods but using a smaller network with less memory and computation requirements. Our approach has been assessed using the DRIVE database.



This work was partly supported by the MINECO/FEDER under TEC2015-64718-R and PSI2015-65848-R projects and the Consejeráa de Innovación, Ciencia y Empresa (Junta de Andalucía, Spain) under the Excellence Project P11-TIC-7103. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPU used for this research.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Andrés Ortiz
    • 1
    Email author
  • Javier Ramírez
    • 2
  • Ricardo Cruz-Arándiga
    • 1
  • María J. García-Tarifa
    • 1
  • Francisco J. Martínez-Murcia
    • 2
  • Juan M. Górriz
    • 2
  1. 1.Communications Engineering DepartmentUniversity of MálagaMálagaSpain
  2. 2.Department of Signal Theory, Communications and NetworkingUniversity of GranadaGranadaSpain

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