Detection of retinal vascular bifurcations by trainable V4-like filters

Authors:
George Azzopardi;Nicolai Petkov
Affiliations:
Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, The Netherlands;Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, The Netherlands
Venue:
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Year:
2011

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Pattern Recognition Letters

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Abstract

The detection of vascular bifurcations in retinal fundus images is important for finding signs of various cardiovascular diseases. We propose a novel method to detect such bifurcations. Our method is implemented in trainable filters that mimic the properties of shape-selective neurons in area V4 of visual cortex. Such a filter is configured by combining given channels of a bank of Gabor filters in an AND-gatelike operation. Their selection is determined by the automatic analysis of a bifurcation feature that is specified by the user from a training image. Consequently, the filter responds to the same and similar bifurcations. With only 25 filters we achieved a correct detection rate of 98.52% at a precision rate of 95.19% on a set of 40 binary fundus images, containing more than 5000 bifurcations. In principle, all vascular bifurcations can be detected if a sufficient number of filters are configured and used.