Multiresolution elastic matching
Computer Vision, Graphics, and Image Processing
A survey of image registration techniques
ACM Computing Surveys (CSUR)
Landmark-based registration using features identified through differential geometry
Handbook of medical imaging
Across-modality registration using intensity-based cost functions
Handbook of medical imaging
3D Multi-Modality Medical Image Registration Using Feature Space Clustering
CVRMed '95 Proceedings of the First International Conference on Computer Vision, Virtual Reality and Robotics in Medicine
Non-rigid Multimodal Image Registration Using Mutual Information
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Multimodality Deformable Registration of Pre- and Intraoperative Images for MRI-guided Brain Surgery
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Multi-modal Volume Registration Using Joint Intensity Distributions
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Non-rigid Registration of Breast MR Images Using Mutual Information
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Gray-Value Based Registration of CT and MR Images by Maximization of Local Correlation
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
An Extensible MRI Simulator for Post-Processing Evaluation
VBC '96 Proceedings of the 4th International Conference on Visualization in Biomedical Computing
Fast Fluid Registration of Medical Images
VBC '96 Proceedings of the 4th International Conference on Visualization in Biomedical Computing
Alignment by maximization of mutual information
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Information Theoretic Deformable Registration Using Local Image Information
International Journal of Computer Vision
Evaluation of Brain MRI Alignment with the Robust Hausdorff Distance Measures
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Multi-modal diffeomorphic demons registration based on point-wise mutual information
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Maximum a posteriori local histogram estimation for image registration
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Non-local shape descriptor: a new similarity metric for deformable multi-modal registration
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
MICCAI'11 Proceedings of the 2011 international conference on Prostate cancer imaging: image analysis and image-guided interventions
Bayesian characterization of uncertainty in multi-modal image registration
WBIR'12 Proceedings of the 5th international conference on Biomedical Image Registration
Local joint entropy based non-rigid multimodality image registration
Pattern Recognition Letters
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High-dimensional non-rigid registration of multi-modal data requires similarity measures with two important properties: multi-modality and locality. Unfortunately all commonly used multi-modal similarity measures are inherently global and cannot operate on small image regions. In this paper, we propose a new class of multi-modal similarity measures, which are constructed from information of the whole images but can be applied pointwise. Due to their capability of measuring correspondence for individual image points we call them point similarity measures. Point similarity measures can be derived from global measures and enable detailed relative comparison of local image correspondence. We present a set of multimodal point similarity measures based on joint intensity distribution and test them as an integral part of non-rigid multi-modal registration system. The comparison results show that segmentation-based measure, which models the joint distribution as a sum of intensity classes, performs best. When intensity classes do not exist or cannot be accurately modeled, each intensity pair can be treated as a separate class, which results in a more general measure, suitable for various non-rigid registration tasks.