Biologically motivated computationally intensive approaches to image pattern recognition
Future Generation Computer Systems - Special double issue: high performance computing and networking (HPCN)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
ICHIT '06 Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 02
Automatic Detection of Vascular Bifurcations and Crossovers from Color Retinal Fundus Images
SITIS '07 Proceedings of the 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System
Detection of retinal vascular bifurcations by trainable V4-like filters
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
Hi-index | 0.00 |
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.