Elements of information theory
Elements of information theory
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Multi-modal Volume Registration Using Joint Intensity Distributions
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
A flexible registration and evaluation engine (f.r.e.e.)
Computer Methods and Programs in Biomedicine
Diverse Evolutionary Neural Networks Based on Information Theory
Neural Information Processing
A marginalized MAP approach and EM optimization for pair-wise registration
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Registration of cardiac SPECT/CT data through weighted intensity co-occurrence priors
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Image transport regression using mixture of experts and discrete Markov random fields
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
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
Multimodal registration via spatial-context mutual information
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Multi-modal image registration using dirichlet-encoded prior information
WBIR'06 Proceedings of the Third international conference on Biomedical Image Registration
Evaluating similarity measures for brain image registration
Journal of Visual Communication and Image Representation
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In this paper, we propose a multi-modal image registration method based on the a priori knowledge of the expected joint intensity distribution estimated from aligned training images. The goal of the registration is to find the optimal transformation such that the discrepancy between the expected and the observed joint intensity distributions is minimised. The difference between distributions is measured using the Kullback-Leibler distance (KLD). Experimental results in 3D-3D registration show that the KLD based registration algorithm is less dependent on the size of the sampling region than the Maximum log-Likelihood based registration method. We have also shown that, if manual alignment is unavailable, the expected joint intensity distribution can be estimated based on the segmented and corresponding structures from a pair of novel images. The proposed method has been applied to 2D-3D registration problems between digital subtraction angiograms (DSAs) and magnetic resonance angiographic (MRA) image volumes.