Multi-modality liver image registration based on multilevel B-splines free-form deformation and L-BFGS optimal algorithm  

作　　者：宋红 李佳佳 王树良 马婧婷 

机构地区：[1]School of Software,Beijing Institute of Technology [2]School of Computer Science,Beijing Institute of Technology

出　　处：《Journal of Central South University》2014年第1期287-292,共6页中南大学学报（英文版）

基　　金：Project(61240010)supported by the National Natural Science Foundation of China;Project(20070007070)supported by Specialized Research Fund for the Doctoral Program of Higher Education of China

摘　　要：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.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.

关 键 词：multi-modal image registration affine transformation B-splines free-form deformation （FFD） L-BFGS 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] O242.23[自动化与计算机技术—计算机科学与技术]