SIAM Journal on Scientific and Statistical Computing
A limited memory algorithm for bound constrained optimization
SIAM Journal on Scientific Computing
Alignment by Maximization of Mutual Information
International Journal of Computer Vision
Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization
ACM Transactions on Mathematical Software (TOMS)
Variational Methods for Multimodal Image Matching
International Journal of Computer Vision
Image Metamorphosis with Scattered Feature Constraints
IEEE Transactions on Visualization and Computer Graphics
Improved Fast Gauss Transform and Efficient Kernel Density Estimation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
A mutual information extension to the matched filter
Signal Processing - Special issue: Information theoretic signal processing
Non-Rigid Multi-Modal Image Registration Using Cross-Cumulative Residual Entropy
International Journal of Computer Vision
WBIR'06 Proceedings of the Third international conference on Biomedical Image Registration
Cumulative residual entropy: a new measure of information
IEEE Transactions on Information Theory
Optimization of mutual information for multiresolution image registration
IEEE Transactions on Image Processing
Motion compensation algorithm based on color orientation codes and covariance matching
ICIRA'10 Proceedings of the Third international conference on Intelligent robotics and applications - Volume Part II
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The use of information theoretic measures (ITMs) has been steadily growing in image processing, bioinformatics, and pattern classification. Although the ITMs have been extensively used in rigid and affine registration of multi-modal images, their computation and accuracy are critical issues in deformable image registration. Three important aspects of using ITMs in multi-modal deformable image registration are considered in this paper: computation, inverse consistency, and accuracy; a symmetric formulation of the deformable image registration problem through the computation of derivatives and resampling on both source and target images, and sufficient criteria for inverse consistency are presented for the purpose of achieving more accurate registration. The techniques of estimating ITMs are examined and analytical derivatives are derived for carrying out the optimization in a computationally efficient manner. ITMs based on Shannon's and Renyi's definitions are considered and compared. The obtained evaluation results via registration functions, and controlled deformable registration of multi-modal digital brain phantom and in vivo magnetic resonance brain images show the improved accuracy and efficiency of the developed formulation. The results also indicate that despite the recent favorable studies towards the use of ITMs based on Renyi's definitions, these measures are seen not to provide improvements in this type of deformable registration as compared to ITMs based on Shannon's definitions.