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Tree Shape Priors with Connectivity Constraints Using Convex Relaxation on General Graphs

Stühmer, Jan and Schröder, Peter and Cremers, Daniel (2013) Tree Shape Priors with Connectivity Constraints Using Convex Relaxation on General Graphs. In: 2013 IEEE International Conference on Computer Vision. IEEE , Piscataway, NJ, pp. 2336-2343. ISBN 978-1-4799-2840-8.

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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.

Item Type:Book Section
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URLURL TypeDescription
Schröder, Peter0000-0002-0323-7674
Cremers, Daniel0000-0002-3079-7984
Additional Information:This research was supported by the ERC Starting Grant “ConvexVision” and the Technische Universität München - Institute for Advanced Study, funded by the German Excellence Initiative.
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European Research Council (ERC)UNSPECIFIED
Technische Universität MünchenUNSPECIFIED
Record Number:CaltechAUTHORS:20230210-663266000.1
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:119199
Deposited By: George Porter
Deposited On:10 Feb 2023 23:41
Last Modified:10 Feb 2023 23:41

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