Sparse Kernel Machines for Discontinuous Registration and Nonstationary Regularization

Jud, Christoph and Moeri, Nadia and Cattin, Philippe C.. (2016) Sparse Kernel Machines for Discontinuous Registration and Nonstationary Regularization. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. pp. 449-456.

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Official URL: http://edoc.unibas.ch/54590/

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We present a novel approach where we address image registration with the concept of a sparse kernel machine. We formulate the registration problem as a regularized minimization functional where a reproducing kernel Hilbert space is used as transformation model. The regularization comprises a sparsity inducing l1-type norm and a well known l2 norm. We prove a representer theorem for this type of functional to guarantee a finite dimensional solution. The presented method brings the advantage of flexibly defining the admissible transformations by choosing a positive definite kernel jointly with an efficient sparse representation of the solution. As such, we introduce a new type of kernel function, which enables discontinuities in the transformation and simultaneously has nice interpolation properties. In addition, location-dependent smoothness is achieved within the same framework to further improve registration results. Finally, we make use of an adaptive grid refinement scheme to optimize on multiple scales and for a finer control point grid at locations of high gradients. We evaluate our new method with a public thoracic 4DCT dataset.
Faculties and Departments:03 Faculty of Medicine > Departement Biomedical Engineering > Imaging and Computational Modelling > Center for medical Image Analysis & Navigation (Cattin)
UniBasel Contributors:Cattin, Philippe Claude
Item Type:Conference or Workshop Item, refereed
Conference or workshop item Subtype:Conference Paper
Publisher:IEEE Xplore Digital Library
Note:Publication type according to Uni Basel Research Database: Conference paper
Last Modified:31 Jan 2020 14:00
Deposited On:30 Nov 2017 16:58

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