Tree shape priors with connectivity constraints using convex relaxation on general graphs
Stuehmer J, Schroeder P, Cremers D (2013)
Publication Type: Conference contribution
Publication year: 2013
Publisher: Institute of Electrical and Electronics Engineers Inc.
Pages Range: 2336-2343
Conference Proceedings Title: Proceedings of the IEEE International Conference on Computer Vision
Event location: AUS
ISBN: 9781479928392
Abstract
In this work we propose a novel method to include a connectivity prior into image segmentation that is based on a binary labeling of a directed graph, in this case a geodesic shortest path tree. Specifically we make two contributions: First, we construct a geodesic shortest path tree with a distance measure that is related to the image data and the bending energy of each path in the tree. Second, we include a connectivity prior in our segmentation model, that allows to segment not only a single elongated structure, but instead a whole connected branching tree. Because both our segmentation model and the connectivity constraint are convex a global optimal solution can be found. To this end, we generalize a recent primal-dual algorithm for continuous convex optimization to an arbitrary graph structure. To validate our method we present results on data from medical imaging in angiography and retinal blood vessel segmentation. © 2013 IEEE.
Involved external institutions
Technische Universität München (TUM)
Germany (DE)
California Institute of Technology (Caltech)
United States (USA) (US)
Germany (DE)
California Institute of Technology (Caltech)
United States (USA) (US)
How to cite
APA:
Stuehmer, J., Schroeder, P., & Cremers, D. (2013). Tree shape priors with connectivity constraints using convex relaxation on general graphs. In Proceedings of the IEEE International Conference on Computer Vision (pp. 2336-2343). AUS: Institute of Electrical and Electronics Engineers Inc..
MLA:
Stuehmer, Jan, Peter Schroeder, and Daniel Cremers. "Tree shape priors with connectivity constraints using convex relaxation on general graphs." Proceedings of the 2013 14th IEEE International Conference on Computer Vision, ICCV 2013, AUS Institute of Electrical and Electronics Engineers Inc., 2013. 2336-2343.
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