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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| Main Authors: | , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
11 May 2016
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| In: |
IEEE transactions on image processing
Year: 2016, Volume: 25, Issue: 7, Pages: 3384-3394 |
| ISSN: | 1941-0042 |
| DOI: | 10.1109/TIP.2016.2567068 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1109/TIP.2016.2567068
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| Author Notes: | Anca Stefanoiu, Andreas Weinmann, Martin Storath, Nassir Navab, Maximilian Baust |
| Summary: | 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. |
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| Item Description: | Gesehen am 15.05.2020 |
| Physical Description: | Online Resource |
| ISSN: | 1941-0042 |
| DOI: | 10.1109/TIP.2016.2567068 |