On a New Diffeomorphic Multi-Modality Image Registration Model and Its Convergent Gauss-Newton Solver  

作　　者：Daoping ZHANG Anis THELJANI Ke CHEN 

机构地区：[1]EPSRC Liverpool Centre for Mathematics in Healthcare, Centre for Mathematical Imaging Techniques and Department of Mathematical Sciences, The University of Liverpool

出　　处：《Journal of Mathematical Research with Applications》2019年第6期633-656,共24页数学研究及应用（英文版）

摘　　要：In this work, we propose a new variational model for multi-modal image registration and present an efficient numerical implementation. The model minimizes a new functional based on using reformulated normalized gradients of the images as the fidelity term and higher-order derivatives as the regularizer. A key feature of the model is its ability of guaranteeing a diffeomorphic transformation which is achieved by a control term motivated by the quasi-conformal map and Beltrami coefficient. The existence of the solution of this model is established. To solve the model numerically, we design a Gauss-Newton method to solve the resulting discrete optimization problem and prove its convergence;a multilevel technique is employed to speed up the initialization and avoid likely 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.In this work, we propose a new variational model for multi-modal image registration and present an efficient numerical implementation. The model minimizes a new functional based on using reformulated normalized gradients of the images as the fidelity term and higher-order derivatives as the regularizer. A key feature of the model is its ability of guaranteeing a diffeomorphic transformation which is achieved by a control term motivated by the quasi-conformal map and Beltrami coefficient. The existence of the solution of this model is established. To solve the model numerically, we design a Gauss-Newton method to solve the resulting discrete optimization problem and prove its convergence; a multilevel technique is employed to speed up the initialization and avoid likely 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.

关 键 词：MULTI-MODAL image registration VARIATIONAL MODEL diffeomorphic transformation 

分 类 号：O17[理学—数学]