TY - GEN
T1 - Multi-modality Image Registration Models and Efficient Algorithms
AU - Zhang, Daoping
AU - Theljani, Anis
AU - Chen, Ke
N1 - Publisher Copyright: © 2021, Springer Nature Singapore Pte Ltd.
Zhang, D., Theljani, A., Chen, K. (2021). Multi-modality Image Registration Models and Efficient Algorithms. In: Tai, XC., Wei, S., Liu, H. (eds) Mathematical Methods in Image Processing and Inverse Problems. IPIP 2018. Springer Proceedings in Mathematics & Statistics, vol 360. Springer, Singapore. https://doi.org/10.1007/978-981-16-2701-9_3
PY - 2021/9/26
Y1 - 2021/9/26
N2 - In this Chapter we discuss multi-modality image registration models and efficient algorithms. We propose a simple method to enhance a variational model to generate a diffeomorphic transformation. The idea is illustrated by using a particular model based on reformulated normalized gradients of the images as the fidelity term and higher-order derivatives as the regularizer. By adding a control term motivated by quasi-conformal maps and Beltrami coefficients, the model has the ability to guarantee a diffeomorphic transformation. Without this feature, the model may lead to visually pleasing but invalid results. To solve the model numerically, we present both a Gauss-Newton method and an augmented Lagrangian method to solve the resulting discrete optimization problem. A multilevel technique is employed to speed up the initialization and reduce the possibility of getting local minima of the underlying functional. Finally numerical experiments demonstrate that this new model can deliver good performances for multi-modal image registration and simultaneously generate an accurate diffeomorphic transformation.
AB - In this Chapter we discuss multi-modality image registration models and efficient algorithms. We propose a simple method to enhance a variational model to generate a diffeomorphic transformation. The idea is illustrated by using a particular model based on reformulated normalized gradients of the images as the fidelity term and higher-order derivatives as the regularizer. By adding a control term motivated by quasi-conformal maps and Beltrami coefficients, the model has the ability to guarantee a diffeomorphic transformation. Without this feature, the model may lead to visually pleasing but invalid results. To solve the model numerically, we present both a Gauss-Newton method and an augmented Lagrangian method to solve the resulting discrete optimization problem. A multilevel technique is employed to speed up the initialization and reduce the possibility of getting local minima of the underlying functional. Finally numerical experiments demonstrate that this new model can deliver good performances for multi-modal image registration and simultaneously generate an accurate diffeomorphic transformation.
KW - diffeomorphic transformation
KW - multi-modal image registration
KW - variational model
KW - efficient algorithms
KW - quasi-conformal maps
KW - Beltrami coefficients
KW - discrete optimization problem
UR - http://www.scopus.com/inward/record.url?scp=85116486319&partnerID=8YFLogxK
U2 - 10.1007/978-981-16-2701-9_3
DO - 10.1007/978-981-16-2701-9_3
M3 - Conference contribution book
AN - SCOPUS:85116486319
SN - 9789811627002
VL - 360
T3 - Springer Proceedings in Mathematics and Statistics
SP - 33
EP - 60
BT - Mathematical Methods in Image Processing and Inverse Problems, IPIP 2018
A2 - Tai, Xue-Cheng
A2 - Wei, Suhua
A2 - Liu, Haiguang
PB - Springer
CY - Singapore
T2 - International Workshop on Image Processing and Inverse Problems, IPIP 2018
Y2 - 21 April 2018 through 24 April 2018
ER -