Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization
ACM Transactions on Mathematical Software (TOMS)
Multi-modal Image Registration by Minimising Kullback-Leibler Distance
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II
Learning based non-rigid multi-modal image registration using Kullback-Leibler divergence
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Optimization of mutual information for multiresolution image registration
IEEE Transactions on Image Processing
Computer Vision and Image Understanding
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Multi-modal non-rigid image registration is widely used in different areas, including medical image analysis and image processing. In this paper, we introduce a new learningbased method for non-rigid image registration. The proposed method is based on a priori knowledge of the joint intensity distribution of a pre-aligned image pair. The similarity and dissimilarity of the expected and observed joint intensity distributions are measured by two Kullback-Leibler distances (KLD). Free-Form Deformation (FFD) is employed as the transformation model along with the L-BFGS-B optimizer. The derivatives of KLDs are derived to work with the L-BFGS-B optimizer. Moreover, we have tested our method with CT-TI image pairs and compared the results obtained by using the mutual information based FFD and the conventional KLD based FFD. The experimental results show that our method gives remarkable improvement on the registration quality.