Home
Login
Deutsch
Advanced Search
Browse by ...
Publication overview
Contact persons
Help
DOI
PMC
 |
|
Open Access Green as soon as Postprint is submitted to ZB.
Joint segmentation and shape regularization with a generalized forward backward algorithm.
IEEE Trans. Image Process. 25, 3384-3394 (2016)
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.
Altmetric
Additional Metrics?
Edit extra informations
Login
Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Object Segmentation ; Image Sequence Analysis; Sobolev Active Contours; Manifold-valued Data; Plane-curves; Spaces; Tracking; Metrics; Registration; Objects; Images; Energy
ISSN (print) / ISBN
1057-7149
e-ISSN
1941-0042
Quellenangaben
Volume: 25,
Issue: 7,
Pages: 3384-3394
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Publishing Place
Piscataway
Reviewing status
Peer reviewed
Institute(s)
Institute of Computational Biology (ICB)