A Coarse-to-Fine Deformable Contour Optimization Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
Iconic feature based nonrigid registration: the PASHA algorithm
Computer Vision and Image Understanding - Special issue on nonrigid image registration
Matching of medical images by self-organizing neural networks
Pattern Recognition Letters
Parametric estimation of affine deformations of planar shapes
Pattern Recognition
Topology preservation evaluation of compact-support radial basis functions for image registration
Pattern Recognition Letters
Topology-preserving registration: a solution via graph cuts
IWCIA'11 Proceedings of the 14th international conference on Combinatorial image analysis
Hierarchical vs. simultaneous multiresolution strategies for nonrigid image registration
WBIR'12 Proceedings of the 5th international conference on Biomedical Image Registration
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In this paper, we address the issue of topology preservation in deformable image matching. A novel constrained hierarchical parametric approach is presented, that ensures that the mapping is globally one-to one and thus preserves topology in the deformed image. The transformation between the source and target images is parameterized at different scales, using a decomposition of the deformation vector field over a sequence of nested (multiresolution) subspaces. The Jacobian of the mapping is controlled over the continuous domain of the transformation, ensuring actual topology preservation on the whole image support. The resulting fast nonlinear constrained optimization algorithm enables to track large nonlinear deformations while preserving the topology. Experimental results are presented both on simulated data and on real medical images