Retinal vessel segmentation using multi-scale textons derived from keypoints

Zhang, Lei, Fisher, Mark and Wang, Wenjia (2015) Retinal vessel segmentation using multi-scale textons derived from keypoints. Computerized Medical Imaging and Graphics, 45 . ISSN 0895-6111

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Item Type:Article
Item Status:Live Archive


This paper presents a retinal vessel segmentation algorithm which uses a texton dictionary to classify vessel/non-vessel pixels. However, in contrast to previous work where filter parameters are learnt from manually labelled image pixels our filter parameters are derived from a smaller set of image features that we call keypoints. A Gabor filter bank, parameterised empirically by ROC analysis, is used to extract keypoints representing significant scale specific vessel features using an approach inspired by the SIFT algorithm. We first determine keypoints using a validation set and then derive seeds from these points to initialise a k-means clustering algorithm which builds a texton dictionary from another training set. During testing we use a simple 1-NN classifier to identify vessel/non-vessel pixels and evaluate our system using the DRIVE database. We achieve average values of sensitivity, specificity and accuracy of 78.12%, 96.68% and 95.05% respectively. We find that clusters of filter responses from keypoints are more robust than those derived from hand-labelled pixels. This, in turn yields textons more representative of vessel/non-vessel classes and mitigates problems arising due to intra and inter-observer variability.

Keywords:Retinal vessels, image segmentation, Texton, keypoints, bmjgoldcheck, NotOAChecked
Subjects:F Physical Sciences > F350 Medical Physics
Divisions:College of Science > School of Computer Science
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ID Code:18058
Deposited On:28 Jul 2015 15:02

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