Multi-modality liver image registration based on multilevel B-splines free-form deformation and L-BFGS optimal algorithm
来源期刊：中南大学学报(英文版)2014年第1期
论文作者：SONG Hong(宋红) LI Jia-jia(李佳佳) WANG Shu-liang(王树良) MA Jing-ting(马婧婷)
文章页码：287 - 292
Key words：multi-modal image registration; affine transformation; B-splines free-form deformation (FFD); L-BFGS
Abstract: A new coarse-to-fine strategy was proposed for nonrigid registration of computed tomography (CT) and magnetic resonance (MR) images of a liver. This hierarchical framework consisted of an affine transformation and a B-splines free-form deformation (FFD). The affine transformation performed a rough registration targeting the mismatch between the CT and MR images. The B-splines FFD transformation performed a finer registration by correcting local motion deformation. In the registration algorithm, the normalized mutual information (NMI) was used as similarity measure, and the limited memory Broyden-Fletcher- Goldfarb-Shannon (L-BFGS) optimization method was applied for optimization process. The algorithm was applied to the fully automated registration of liver CT and MR images in three subjects. The results demonstrate that the proposed method not only significantly improves the registration accuracy but also reduces the running time, which is effective and efficient for nonrigid registration.
SONG Hong(宋红)1, LI Jia-jia(李佳佳)2, WANG Shu-liang(王树良)1, MA Jing-ting(马婧婷)1
(1. School of Software, Beijing Institute of Technology, Beijing 100081, China;
2. School of Computer Science, Beijing Institute of Technology, Beijing 100081, China)
Abstract:A new coarse-to-fine strategy was proposed for nonrigid registration of computed tomography (CT) and magnetic resonance (MR) images of a liver. This hierarchical framework consisted of an affine transformation and a B-splines free-form deformation (FFD). The affine transformation performed a rough registration targeting the mismatch between the CT and MR images. The B-splines FFD transformation performed a finer registration by correcting local motion deformation. In the registration algorithm, the normalized mutual information (NMI) was used as similarity measure, and the limited memory Broyden-Fletcher- Goldfarb-Shannon (L-BFGS) optimization method was applied for optimization process. The algorithm was applied to the fully automated registration of liver CT and MR images in three subjects. The results demonstrate that the proposed method not only significantly improves the registration accuracy but also reduces the running time, which is effective and efficient for nonrigid registration.
Key words:multi-modal image registration; affine transformation; B-splines free-form deformation (FFD); L-BFGS
