Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Fast Intensity-based 2D-3D Image Registration of Clinical Data Using Light Fields
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Parametric correspondence and chamfer matching: two new techniques for image matching
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
Extended global optimization strategy for rigid 2D/3D image registration
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Fast 2d-3d point-based registration using GPU-Based preprocessing for image-guided surgery
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
A GPGPU approach for accelerating 2-d/3-d rigid registration of medical images
ISPA'06 Proceedings of the 4th international conference on Parallel and Distributed Processing and Applications
Quantification of Bone Remodeling in SRμCT Images of Implants
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Computer Methods and Programs in Biomedicine
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To provide better insight in bone modeling and remodeling around implants, information is extracted using different imaging techniques. Two types of data used in this project are 2D histological images and 3D SRμCT (synchrotron radiation-based computed microtomography) volumes. To enable a direct comparison between the two modalities and to bypass the time consuming and difficult task of manual annotation of the volumes, registration of these data types is desired. In this paper, we present two 2D---3D intermodal rigid-body registration methods for the mentioned purpose. One approach is based on Simulated Annealing (SA) while the other uses Chamfer Matching (CM). Both methods use Normalized Mutual Information for measuring the correspondence between an extracted 2D-slice from the volume and the 2D histological image whereas the latter approach also takes the edge distance into account for matching the implant boundary. To speed up the process, part of the computations are done on the Graphic Processing Unit. The results show that the CM-approach provides a more reliable registration than the SA-approach. The registered slices with the CM-approach correspond visually well to the histological sections, except for cases where the implant has been damaged.