A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Stochastic Iterative Closest Point Algorithm (stochastICP)
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Nonrigid 3-D/2-D Registration of Images Using Statistical Models
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
A new point matching algorithm for non-rigid registration
Computer Vision and Image Understanding - Special issue on nonrigid image registration
Robust Point Matching for Two-Dimensional Nonrigid Shapes
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
ISBMS '08 Proceedings of the 4th international symposium on Biomedical Simulation
ICDHM'07 Proceedings of the 1st international conference on Digital human modeling
Hi-index | 0.00 |
The application of fluoroscopic images in operation is pervasive, especially for orthopaedic surgery. Anatomy-based 3D/2D registration, rigid or non-rigid, has been proven to improve the accuracy and precision of various image-guided therapies. One of the key steps for a successful anatomy-based registration is to establish 3D/2D correspondence between the 3D model and the 2D images. This paper presents a novel 3D/2D correspondence building method based on a non-rigid 2D point matching process, which iteratively uses a symmetric injective nearest-neighbor mapping operator and 2D thin-plate spline based deformation to find a fraction of best matched 2D point pairs between features detected from the X-ray images and those extracted from the 3D model. The estimated point pairs are further ranked by their shape context matching cost and those with high cost are eliminated. The remaining point pairs are then used to set up a set of 3D point pairs such that we turn a 3D/2D registration problem to a 3D/3D one, whose solutions are well studied. Rigid and non-rigid registration algorithms incorporating the novel 3D/2D correspondence building method are presented. Quantitative and qualitative evaluation results are given, which demonstrate the validity of our method.