TY - GEN
T1 - Log-Demons with driving force for large deformation image registration
AU - Zhang, Le
AU - Wen, Ying
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/10/31
Y1 - 2016/10/31
N2 - Capturing large diffeomorphic deformations is difficult for many non-rigid registration methods. In this paper, we propose Log-Demons with driving force for large deformation image registration. The driving force obtained by boundary points correspondence exerts influence on continuous optimization of Log-Demons to improve the motion direction of points. We utilize MROGH descriptor matching to obtain points correspondence as driving force, then the driving force is added to the optimization of Log-Demons. We integrate the driving force in an exponentially decreasing form with velocity field of Log-Demons to drive the points moving globally and to speed up the convergence. Experiments performed on synthetic images, real scene images and brain images demonstrate that the proposed method can not only capture large deformations but also preserve details and register images at a higher accuracy.
AB - Capturing large diffeomorphic deformations is difficult for many non-rigid registration methods. In this paper, we propose Log-Demons with driving force for large deformation image registration. The driving force obtained by boundary points correspondence exerts influence on continuous optimization of Log-Demons to improve the motion direction of points. We utilize MROGH descriptor matching to obtain points correspondence as driving force, then the driving force is added to the optimization of Log-Demons. We integrate the driving force in an exponentially decreasing form with velocity field of Log-Demons to drive the points moving globally and to speed up the convergence. Experiments performed on synthetic images, real scene images and brain images demonstrate that the proposed method can not only capture large deformations but also preserve details and register images at a higher accuracy.
UR - https://www.scopus.com/pages/publications/85007168703
U2 - 10.1109/IJCNN.2016.7727587
DO - 10.1109/IJCNN.2016.7727587
M3 - 会议稿件
AN - SCOPUS:85007168703
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 3052
EP - 3059
BT - 2016 International Joint Conference on Neural Networks, IJCNN 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 International Joint Conference on Neural Networks, IJCNN 2016
Y2 - 24 July 2016 through 29 July 2016
ER -