TY - GEN
T1 - Temporal groupwise registration for motion modeling
AU - Yigitsoy, Mehmet
AU - Wachinger, Christian
AU - Navab, Nassir
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/80052340499
U2 - 10.1007/978-3-642-22092-0_53
DO - 10.1007/978-3-642-22092-0_53
M3 - Conference contribution
C2 - 21761693
AN - SCOPUS:80052340499
SN - 9783642220913
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 648
EP - 659
BT - Information Processing in Medical Imaging - 22nd International Conference, IPMI 2011, Proceedings
T2 - 22nd International Conference on Information Processing in Medical Imaging, IPMI 2011
Y2 - 3 July 2011 through 8 July 2011
ER -