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GPU-based segmentation of retinal blood vessels

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Abstract

In this paper a fast and accurate technique for retinal vessel tree extraction is proposed. It consists of a hybrid strategy based on global image filtering and contour tracing. With the aim of increasing the computation speed, the algorithm has been tailored for efficient execution on commodity graphics processing units achieving low execution times and high speedups over the CPU execution. The performance of the proposed method was tested on publicly available databases, STARE and DRIVE, based on standard measures such as accuracy, sensitivity and specificity. Results reveal an average accuracy comparable to that reported for state-of-the art techniques. Our method performs the vascular tree segmentation of the images in the DRIVE and the STARE databases in an average of 14 ms and 18 ms, respectively. To the best of our knowledge, the proposal features the highest accuracy/performance rate in the retinal blood vessel extraction domain.

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Notes

  1. Code available in http://wiki.citius.usc.es/software/gpu-retina.

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Acknowledgments

This work was supported in part by the Ministry of Science and Innovation, Government of Spain, and co-funded by European Union ERDF, under contract TIN 2010-17541.

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Authors and Affiliations

  1. CITIUS, Campus Vida, Universidad de Santiago de Compostela, 15782, Santiago de Compostela, La Coruña, Spain

    Francisco Argüello, David L. Vilariño, Dora B. Heras & Alejandro Nieto

Authors
  1. Francisco Argüello
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  2. David L. Vilariño
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  3. Dora B. Heras
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  4. Alejandro Nieto
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Correspondence to Dora B. Heras.

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Argüello, F., Vilariño, D.L., Heras, D.B. et al. GPU-based segmentation of retinal blood vessels. J Real-Time Image Proc 14, 773–782 (2018). https://doi.org/10.1007/s11554-014-0469-z

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  • DOI: https://doi.org/10.1007/s11554-014-0469-z

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