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.
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Foundation item: 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
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Song, H., Li, Jj., Wang, Sl. et al. Multi-modality liver image registration based on multilevel B-splines free-form deformation and L-BFGS optimal algorithm. J. Cent. South Univ. 21, 287–292 (2014). https://doi.org/10.1007/s11771-014-1939-y
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DOI: https://doi.org/10.1007/s11771-014-1939-y