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A real-time fuzzy morphological algorithm for retinal vessel segmentation

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Abstract

The detection of vessels is the first step towards an automatic diagnosis and in-depth study of retinal images to aid ophthalmologists. In this paper, a real-time algorithm based on fuzzy morphological techniques is introduced to segment vessels in retinal images. This framework provides a good trade-off between expressive power and computational requirements, since the information in the local neighbourhood is quickly processed by combining a series of fast procedures. Specifically, this method is based on the fuzzy black top-hat transform, which proves to be a simple yet very effective technique. The algorithm processes images of the DRIVE and STARE datasets, in average, in 37 and 57 ms, respectively. Thus, it can be employed while a patient is being examined, embedded into more complex systems or as a pre-screening method for large volumes of data. It outstands when it is compared with other state-of-the-art methodologies in terms of its real-time processing time and its competitive performance.

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Acknowledgements

This work has been partially supported by the project TIN 2016-75404-P. P. Bibiloni also benefited from the fellowship FPI/1645/2014 from the Conselleria d’Educació, Cultura i Universitats of the Govern de les Illes Balears under an operational programme co-financed by the European Social Fund.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of the Balearic Islands, 07122, Palma, Spain

    Pedro Bibiloni, Manuel González-Hidalgo & Sebastia Massanet

  2. Balearic Islands Health Research Institute (IdISBa), 07010, Palma, Spain

    Pedro Bibiloni, Manuel González-Hidalgo & Sebastia Massanet

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  1. Pedro Bibiloni
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  2. Manuel González-Hidalgo
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  3. Sebastia Massanet
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Correspondence to Pedro Bibiloni.

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Bibiloni, P., González-Hidalgo, M. & Massanet, S. A real-time fuzzy morphological algorithm for retinal vessel segmentation. J Real-Time Image Proc 16, 2337–2350 (2019). https://doi.org/10.1007/s11554-018-0748-1

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  • DOI: https://doi.org/10.1007/s11554-018-0748-1

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