Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Markov random field modeling in computer vision
Markov random field modeling in computer vision
On visible surface generation by a priori tree structures
SIGGRAPH '80 Proceedings of the 7th annual conference on Computer graphics and interactive techniques
Alignment by maximization of mutual information
Alignment by maximization of mutual information
Compact representations for fast nonrigid registration of medical images
Compact representations for fast nonrigid registration of medical images
Multi-modal image registration by quantitative-qualitative measure of mutual information (Q-MI)
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
An overlapping tree approach to multiscale stochastic modeling and estimation
IEEE Transactions on Image Processing
Discrete Markov image modeling and inference on the quadtree
IEEE Transactions on Image Processing
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We present a new method for the fast and robust computation of information theoretic similarity measures for alignment of multi-modality medical images. The proposed method defines a non-uniform, adaptive sampling scheme for estimating the entropies of the images, which is less vulnerable to local maxima as compared to uniform and random sampling. The sampling is defined using an octree partition of the template image, and is preferable over other proposed methods of non-uniform sampling since it respects the underlying data distribution. It also extends naturally to a multi-resolution registration approach, which is commonly employed in the alignment of medical images. The effectiveness of the proposed method is demonstrated using both simulated MR images obtained from the BrainWeb database and clinical CT and SPECT images.