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Unsupervised Retinal Vessel Segmentation Using Combined Filters

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  • Wendeson S Oliveira
  • Joyce Vitor Teixeira
  • Tsang Ing Ren
  • George D C Cavalcanti
  • Jan Sijbers

Abstract

Image segmentation of retinal blood vessels is a process that can help to predict and diagnose cardiovascular related diseases, such as hypertension and diabetes, which are known to affect the retinal blood vessels’ appearance. This work proposes an unsupervised method for the segmentation of retinal vessels images using a combined matched filter, Frangi’s filter and Gabor Wavelet filter to enhance the images. The combination of these three filters in order to improve the segmentation is the main motivation of this work. We investigate two approaches to perform the filter combination: weighted mean and median ranking. Segmentation methods are tested after the vessel enhancement. Enhanced images with median ranking are segmented using a simple threshold criterion. Two segmentation procedures are applied when considering enhanced retinal images using the weighted mean approach. The first method is based on deformable models and the second uses fuzzy C-means for the image segmentation. The procedure is evaluated using two public image databases, Drive and Stare. The experimental results demonstrate that the proposed methods perform well for vessel segmentation in comparison with state-of-the-art methods.

Suggested Citation

  • Wendeson S Oliveira & Joyce Vitor Teixeira & Tsang Ing Ren & George D C Cavalcanti & Jan Sijbers, 2016. "Unsupervised Retinal Vessel Segmentation Using Combined Filters," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-21, February.
    • Handle: RePEc:plo:pone00:0149943
    DOI: 10.1371/journal.pone.0149943
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    References listed on IDEAS

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    1. Peter Bankhead & C Norman Scholfield & J Graham McGeown & Tim M Curtis, 2012. "Fast Retinal Vessel Detection and Measurement Using Wavelets and Edge Location Refinement," PLOS ONE, Public Library of Science, vol. 7(3), pages 1-12, March.
    2. Xiaoxia Yin & Brian W-H Ng & Jing He & Yanchun Zhang & Derek Abbott, 2014. "Accurate Image Analysis of the Retina Using Hessian Matrix and Binarisation of Thresholded Entropy with Application of Texture Mapping," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-17, April.
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