Alignment by Maximization of Mutual Information
International Journal of Computer Vision
EURASIP Journal on Applied Signal Processing
MRI modalitiy transformation in demon registration
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
A generalized divergence measure for robust image registration
IEEE Transactions on Signal Processing
On the convexity of some divergence measures based on entropy functions
IEEE Transactions on Information Theory
Using Spanning Graphs for Efficient Image Registration
IEEE Transactions on Image Processing
Hi-index | 12.05 |
In this paper, an information-theoretic approach for multimodal image registration is presented. In the proposed approach, image registration is carried out by maximizing a Tsallis entropy-based divergence using a modified simultaneous perturbation stochastic approximation algorithm. This divergence measure achieves its maximum value when the conditional intensity probabilities of the transformed target image given the reference image are degenerate distributions. Experimental results are provided to demonstrate the registration accuracy of the proposed approach in comparison to existing entropic image alignment techniques. The feasibility of the proposed algorithm is demonstrated on medical images from magnetic resonance imaging, computer tomography, and positron emission tomography.