Abstract
In this paper, a new feature vector for each pixel, in conjunction with the K-nearest neighbour classifier, is proposed for the segmentation of retinal blood vessels in digital colour fundus images. The proposed feature vector consists of two scale-space features - the largest eigenvalue and the gradient magnitude - of the intensity image, representing the two attributes of any vessel, i.e. the piecewise linearity and parallel edges, as well as the green channel image intensity. In terms of sensitivity and specificity, our results are comparable with other supervised method which uses a set of 31 features, yet in terms of processing time, our method uses a smaller number of features and results in a significant reduction in the processing time.
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