| 1. |
- Strandmark, Petter, et al.
(författare)
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Shortest Paths with Curvature and Torsion
- 2013
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Ingår i: Computer Vision (ICCV), 2013 IEEE International Conference on. - 1550-5499. - 9781479928392 ; , s. 2024-2031
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Konferensbidrag (refereegranskat)abstract
- This paper describes a method of finding thin, elongated structures in images and volumes. We use shortest paths to minimize very general functionals of higher-order curve properties, such as curvature and torsion. Our globally optimal method uses line graphs and its runtime is polynomial in the size of the discretization, often in the order of seconds on a single computer. To our knowledge, we are the first to perform experiments in three dimensions with curvature and torsion regularization. The largest graphs we process have almost one hundred billion arcs. Experiments on medical images and in multi-view reconstruction show the significance and practical usefulness of regularization based on curvature while torsion is still only tractable for small-scale problems
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| 2. |
- Ulén, Johannes, et al.
(författare)
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An Efficient Optimization Framework for Multi-Region Segmentation based on Lagrangian Duality
- 2013
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Ingår i: IEEE Transactions on Medical Imaging. - 1558-254X. ; 32:2, s. 178-188
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Tidskriftsartikel (refereegranskat)abstract
- We introduce a multi-region model for simultaneous segmentation of medical images. In contrast to many other models, geometric constraints such as inclusion and exclusion between the regions are enforced, which makes it possible to correctly segment different regions even if the intensity distributions are identical. We efficiently optimize the model using a combination of graph cuts and Lagrangian duality which is faster and more memory efficient than current state of the art. As the method is based on global optimization techniques, the resulting segmentations are independent of initialization. We apply our framework to the segmentation of the left and right ventricles, myocardium and the left ventricular papillary muscles in MRI and to lung segmentation in full-body X-ray CT. We evaluate our approach on a publicly available benchmark with competitive results.
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