Joint segmentation and shape regularization with a generalized forward-backward algorithm
This paper presents a method for the simultaneous segmentation and regularization of a series of shapes from a corresponding sequence of images. Such series arise as time series of 2D images when considering video data, or as stacks of 2D images obtained by slicewise tomographic reconstruction. We f...
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| Hauptverfasser: | , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
11 May 2016
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| In: |
IEEE transactions on image processing
Year: 2016, Jahrgang: 25, Heft: 7, Pages: 3384-3394 |
| ISSN: | 1941-0042 |
| DOI: | 10.1109/TIP.2016.2567068 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1109/TIP.2016.2567068
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| Verfasserangaben: | Anca Stefanoiu, Andreas Weinmann, Martin Storath, Nassir Navab, Maximilian Baust |
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| 520 | |a This paper presents a method for the simultaneous segmentation and regularization of a series of shapes from a corresponding sequence of images. Such series arise as time series of 2D images when considering video data, or as stacks of 2D images obtained by slicewise tomographic reconstruction. We first derive a model where the regularization of the shape signal is achieved by a total variation prior on the shape manifold. The method employs a modified Kendall shape space to facilitate explicit computations together with the concept of Sobolev gradients. For the proposed model, we derive an efficient and computationally accessible splitting scheme. Using a generalized forward-backward approach, our algorithm treats the total variation atoms of the splitting via proximal mappings, whereas the data terms are dealt with by gradient descent. The potential of the proposed method is demonstrated on various application examples dealing with 3D data. We explain how to extend the proposed combined approach to shape fields which, for instance, arise in the context of 3D+t imaging modalities, and show an application in this setup as well. | ||
| 650 | 4 | |a 2D images | |
| 650 | 4 | |a 3D+t imaging modalities | |
| 650 | 4 | |a Active contours | |
| 650 | 4 | |a Computational modeling | |
| 650 | 4 | |a computerised tomography | |
| 650 | 4 | |a generalized forward-backward algorithm | |
| 650 | 4 | |a gradient descent | |
| 650 | 4 | |a gradient methods | |
| 650 | 4 | |a image reconstruction | |
| 650 | 4 | |a image segmentation | |
| 650 | 4 | |a Image segmentation | |
| 650 | 4 | |a image sequence | |
| 650 | 4 | |a image sequence analysis | |
| 650 | 4 | |a image sequences | |
| 650 | 4 | |a joint segmentation-shape regularization | |
| 650 | 4 | |a Manifolds | |
| 650 | 4 | |a medical image processing | |
| 650 | 4 | |a modified Kendall shape space | |
| 650 | 4 | |a Object segmentation | |
| 650 | 4 | |a proximal mappings | |
| 650 | 4 | |a Shape | |
| 650 | 4 | |a shape signal regularization | |
| 650 | 4 | |a simultaneous segmentation | |
| 650 | 4 | |a slicewise tomographic reconstruction | |
| 650 | 4 | |a Sobolev gradient concept | |
| 650 | 4 | |a splitting scheme | |
| 650 | 4 | |a Three-dimensional displays | |
| 650 | 4 | |a TV | |
| 700 | 1 | |a Storath, Martin |e VerfasserIn |0 (DE-588)1036903818 |0 (DE-627)751410578 |0 (DE-576)389559830 |4 aut | |
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