Variational Methods for Multimodal Image Matching
International Journal of Computer Vision
Non-rigid Multimodal Image Registration Using Mutual Information
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Fast Fluid Registration of Medical Images
VBC '96 Proceedings of the 4th International Conference on Visualization in Biomedical Computing
Deformable templates using large deformation kinematics
IEEE Transactions on Image Processing
Image registration based on kernel-predictability
Computer Vision and Image Understanding
Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication
The time series image analysis of the hela cell using viscous fluid registration
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part III
Kernel-predictability: a new information measure and its application to image registration
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
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We propose a multimodal free form registration algorithm based on maximization of mutual information. Images to be aligned are modeled as a viscous fluid that deforms under the influence of forces derived from the gradient of the mutual information registration criterion. Parzen windowing is used to estimate the joint intensity probability of the images to be matched. The method was verified by for registration of simulated T1-T1, T1-T2 and T1-PD images with known ground truth deformation. The results show that the root mean square difference being the recovered and the ground truth deformation is smaller than 1 voxel.