Adaptive Parametrization of Multivariate B-splines for Image Registration |
Michael Sass Hansen, Ben Glocker, Nassir Navab, Rasmus Larsen
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| Abstract | We present an adaptive parametrization scheme for dynamic mesh refinement in the application of parametric image registration. The scheme is based on a refinement measure ensuring that the control points give an efficient representation of the warp fields, in terms of minimizing the registration cost function. In the current work we introduce multivariate B-splines as a novel alternative to the widely used tensor B-splines enabling us to make efficient use of the derived measure.The multivariate B-splines of order n are Cn- 1 smooth and are based on Delaunay configurations of arbitrary 2D or 3D control point sets. Efficient algorithms for finding the configurations are presented, and B-splines are through their flexibility shown to feature several advantages over the tensor B-splines. In spite of efforts to make the tensor product B-splines more flexible, the knots are still bound to reside on a regular grid. In contrast, by efficient non- constrained placement of the knots, the multivariate B- splines are shown to give a good representation of inho- mogeneous objects in natural settings. The wide applicability of the method is illustrated through its application on medical data and for optical flow estimation. |
| Type | Conference paper [With referee] |
| Conference | IEEE Conference on Computer Vision and Pattern Recognition |
| Year | 2008 pp. 3254 |
| ISBN / ISSN | DOI: 10.1109/CVPR.2008.4587760 |
| BibTeX data | [bibtex] |
| IMM Group(s) | Image Analysis & Computer Graphics |