Digital image processing
Biometric Identification through Hand Geometry Measurements
IEEE Transactions on Pattern Analysis and Machine Intelligence
Muliscale Vessel Enhancement Filtering
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Geometric Level Set Methods in Imaging,Vision,and Graphics
Geometric Level Set Methods in Imaging,Vision,and Graphics
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Robust Centerline Extraction Framework Using Level Sets
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Vessels as 4D Curves: Global Minimal 4D Paths to Extract 3D Tubular Surfaces
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Pattern Recognition
IEEE Transactions on Information Technology in Biomedicine
Rapid automated three-dimensional tracing of neurons from confocal image stacks
IEEE Transactions on Information Technology in Biomedicine
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The blood vessels in the retina have a characteristic radiating pattern, while there exists a significant variation dependent on the individual and/or medical condition. Extracting the geometric properties of these blood vessels have several important applications, such as biometrics (for identification) and medical diagnosis. In this paper, we will focus on biometric applications. For this, we propose a fast and accurate algorithm for tracing the blood vessels, and compare several candidate summary features based on the tracing results. Existing tracing algorithms based on a detailed analysis of the image can be too slow to quickly process a large volume of retinal images in real time (e.g., at a security check point). In order to select good features that can be extracted from the traces, we used kernel Isomap to test the distance between different retinal images as projected onto their respective feature spaces. We tested the following feature set: (1) angle among branches, (2) the number of fiber based on distance, (3) distance between branches, and (4) inner product among branches. Our results indicate that features 3 and 4 are prime candidates for use in fast, realtime biometric tasks. We expect our method to lead to fast and accurate biometric systems based on retinal images.