Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Separation of the retinal vascular graph in arteries and veins based upon structural knowledge
Image and Vision Computing
A robust Graph Transformation Matching for non-rigid registration
Image and Vision Computing
Region and constellations based categorization of images with unsupervised graph learning
Image and Vision Computing
Retinal fundus image registration via vascular structure graph matching
Journal of Biomedical Imaging
Using retinex image enhancement to improve the artery/vein classification in retinal images
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part II
Development of an automated system to classify retinal vessels into arteries and veins
Computer Methods and Programs in Biomedicine
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In this paper, we propose a highly robust point-matching method (Graph Transformation Matching - GTM) relying on finding the consensus graph emerging from putative matches. Such method is a two-phased one in the sense that after finding the consensus graph it tries to complete it as much as possible. We successfully apply GTM to image registration in the context of finding mosaics from retinal images. Feature points are obtained after properly segmenting such images. In addition, we also introduce a novel topological descriptor for quantifying disease by characterizing the arterial/venular trees. Such descriptor relies on diffusion kernels on graphs. Our experiments have showed only statistical significance for the case of arterial trees, which is consistent with previous findings.