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Automated extraction of retinal vasculature
Jen Hong Tan1, U Rajendra Acharya2, Kuang Chua Chua1
1Department of Electronic and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489.
Purpose:
The authors propose an algorithm that automatically extracts retinal vasculature and provides a simple measure to correct the extraction. The output of the method is a network of salient points, and blood vessels are drawn by connecting the salient points using a centripetal parameterized Catmull-Rom spline.
Methods:
The algorithm starts by background correction. The corrected image is filtered with a bank of Gabor kernels, and the responses are consolidated to form a maximal image. After that, the maximal image is thinned to get a network of 1-pixel lines, analyzed and pruned to locate forks and form branches. Finally, the Ramer-Douglas-Peucker algorithm is used to determine salient points. When extraction is not satisfactory, the user simply shifts the salient points to edit the segmentation.
Results:
On average, the authors' extractions cover 93% of ground truths (on the Drive database).
Conclusions:
By expressing retinal vasculature as a series of connected points, the proposed algorithm not only provides a means to edit segmentation but also gives knowledge of the shape of the blood vessels and their connections.
