Abstract
The analysis of the vascular tree in retinal fundus images is important for identifying risks of various cardiovascular diseases. We propose trainable COSFIRE (Combination Of Shifted FIlter REsponses) filters to detect vascular bifurcations. A COSFIRE filter is automatically configured to be selective for a bifurcation that is specified by a user from a training image. The configuration selects given channels of a bank of Gabor filters and determines certain blur and shift parameters. A COSFIRE filter response is computed as the product of the blurred and shifted responses of the selected Gabor filters. The filter responds to bifurcations that are similar to the one used for its configuration. The proposed filters achieve invariance to rotation and scale. With only five COSFIRE filters we achieve a recall of 98.77% at a precision of 95.32% on a data set of 40 binary fundus images (from DRIVE), containing more than 5000 bifurcations.
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Azzopardi, G., Petkov, N. (2012). Detection of Retinal Vascular Bifurcations by Rotation- and Scale-Invariant COSFIRE Filters. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2012. Lecture Notes in Computer Science, vol 7325. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31298-4_43
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DOI: https://doi.org/10.1007/978-3-642-31298-4_43
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