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An Effective Supervised Framework for Retinal Blood Vessel Segmentation Using Local Standardisation and Bagging

  • Uyen T. V. Nguyen
  • Alauddin Bhuiyan
  • Kotagiri Ramamohanarao
  • Laurence A. F. Park
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7009)

Abstract

In this paper, we present a supervised framework for extracting blood vessels from retinal images. The local standardisation of the green channel of the retinal image and the Gabor filter responses at four different scales are used as features for pixel classification. The Bayesian classifier is used with a bagging framework to classify each image pixel as vessel or background. A post processing method is also proposed to correct central reflex artifacts and improve the segmentation accuracy. On the public DRIVE database, our method achieves an accuracy of 0.9491 which is higher than any existing methods. More importantly, visual inspection on the segmentation results shows that our method gives two important improvements on the segmentation quality: vessels are well separated and central reflex are effectively removed. These are important factors that affect to the accuracy of all subsequent vascular analysis.

Keywords

Retinal images blood vessel segmentation central reflex Gabor filter Bayeisan classifier bagging 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Uyen T. V. Nguyen
    • 1
  • Alauddin Bhuiyan
    • 1
  • Kotagiri Ramamohanarao
    • 1
  • Laurence A. F. Park
    • 2
  1. 1.Department of Computer Science and Software EngineeringThe University of MelbourneAustralia
  2. 2.School of Computing and MathematicsUniversity of Western SydneyAustralia

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