An effective supervised framework for retinal blood vessel segmentation using local standardisation and bagging

Authors:
Uyen T. V. Nguyen;Alauddin Bhuiyan;Kotagiri Ramamohanarao;Laurence A. F. Park
Affiliations:
Department of Computer Science and Software Engineering, The University of Melbourne, Australia;Department of Computer Science and Software Engineering, The University of Melbourne, Australia;Department of Computer Science and Software Engineering, The University of Melbourne, Australia;School of Computing and Mathematics, University of Western Sydney, Australia
Venue:
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
Year:
2011

Citing 2
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Bagging predictors

Machine Learning
Adaptive Local Thresholding by Verification-Based Multithreshold Probing with Application to Vessel Detection in Retinal Images

IEEE Transactions on Pattern Analysis and Machine Intelligence

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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.