Alignment by Maximization of Mutual Information
International Journal of Computer Vision
Data Driven Image Models through Continuous Joint Alignment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating myocardial motion by 4D image warping
Pattern Recognition
Spatio-temporal image registration for respiratory motion correction in pet imaging
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Local motion analysis in 4D lung CT using fast groupwise registration
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
WBIR'10 Proceedings of the 4th international conference on Biomedical image registration
Manifold learning for image-based breathing gating with application to 4D ultrasound
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Registration of longitudinal image sequences with implicit template and spatial-temporal heuristics
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Diffeomorphic registration using b-splines
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Efficient population registration of 3d data
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
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We propose a novel method for the registration of time-resolved image sequences, called Spatio-Temporal groupwise non-rigid Registration using free-form deforMations (STORM). It is a groupwise registration method, with a group of images being considered simultaneously, in order to prevent bias introduction. This is different from pairwise registration methods where only two images are registered to each other. Furthermore, STORM is a spatio-temporal registration method, where both, the spatial and the temporal information are utilized during the registration. This ensures the smoothness and consistency of the resulting deformation fields, which is especially important for motion modeling on medical data. Moreover, popular free-form deformations are applied to model the non-rigid motion. Experiments are conducted on both synthetic and medical images. Results show the good performance and the robustness of the proposed approach with respect to outliers and imaging artifacts, and moreover, its ability to correct for larger deformation in comparison to standard pairwise techniques.