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

Jan Stuhmer, Peter Schroder, Daniel Cremers; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2013, pp. 2336-2343

Abstract


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.

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[bibtex]
@InProceedings{Stuhmer_2013_ICCV,
author = {Stuhmer, Jan and Schroder, Peter and Cremers, Daniel},
title = {Tree Shape Priors with Connectivity Constraints Using Convex Relaxation on General Graphs},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2013}
}