A survey of image registration techniques
ACM Computing Surveys (CSUR)
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
An implementation of triangular B-spline surfaces over arbitrary triangulations
Selected papers of the international symposium on Free-form curves and free-form surfaces
Medical image registration incorporating deformations
BMVC '95 Proceedings of the 6th British conference on Machine vision (Vol. 2)
Deformations incorporating rigid structures
Computer Vision and Image Understanding
Efficient evaluation of triangular B-spline surfaces
Computer Aided Geometric Design
Multisubject Non-rigid Registration of Brain MRI Using Intensity and Geometric Features
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
The Correlation Ratio as a New Similarity Measure for Multimodal Image Registration
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
Spline-based elastic image registration: integration of landmark errors and orientation attributes
Computer Vision and Image Understanding
Alignment by maximization of mutual information
Alignment by maximization of mutual information
Surface Reconstruction with Triangular B-splines
GMP '04 Proceedings of the Geometric Modeling and Processing 2004
Membrane nonrigid image registration
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Automated skeleton based multi-modal deformable registration of head&neck datasets
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
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For non-rigid registration, the objects in medical images are usually treated as a single deformable body with homogeneous stiffness distribution. However, this assumption is invalid for certain parts of the human body, where bony structures move rigidly, while the others may deform. In this paper, we introduce a novel registration technique that models local rigidity of pre-identified rigid structures as well as global non-rigidity in the transformation field using triangular B-splines. In contrast to the conventional registration method based on tensor-product B-splines, our approach recovers local rigid transformation with fewer degrees of freedom (DOFs), and accurately simulates sharp features (C0 continuity) along the interface between deformable regions and rigid structures, because of the unique advantages offered by triangular B-splines, such as flexible triangular domain, local control and space-varying smoothness modeling. The accurate matching of the source image with the target one is accomplished through the use of a variational framework, in which a composite energy, measuring the image dissimilarity and enforcing local rigidity and global smoothness, is minimized subject to pre-defined point-based constraints. The algorithm is tested on both synthetic and real 2D images for its applicability. The experimental results show that, by accurately modeling sharp features using triangular B-splines, the deformable regions in the vicinity of rigid structures are less constrained by the global smoothness regularization and therefore contribute extra flexibility to the optimization process. Consequently, the registration quality is improved considerably.