Alignment by Maximization of Mutual Information
International Journal of Computer Vision
The Correlation Ratio as a New Similarity Measure for Multimodal Image Registration
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
An Iterative Approach to Improved Local Phase Coherence Estimation
CRV '08 Proceedings of the 2008 Canadian Conference on Computer and Robot Vision
Intensity gradient based registration and fusion of multi-modal images
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Registration of 3d angiographic and x-ray images using sequential monte carlo sampling
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Jointly registering images in domain and range by piecewise linear comparametric analysis
IEEE Transactions on Image Processing
Efficient Least Squares Multimodal Registration With a Globally Exhaustive Alignment Search
IEEE Transactions on Image Processing
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In this paper, a novel multiresolution algorithm for registering multimodal images, using an adaptive Monte Carlo scheme is presented. At each iteration, random solution candidates are generated from a multidimensional solution space of possible geometric transformations, using an adaptive sampling approach. The generated solution candidates are evaluated based on the Pearson type-VII error between the phase moments of the images to determine the solution candidate with the lowest error residual. The multidimensional sampling distribution is refined with each iteration to produce increasingly more plausible solution candidates for the optimal alignment between the images. The proposed algorithm is efficient, robust to local optima, and does not require manual initialization or prior information about the images. Experimental results based on various real-world medical images show that the proposed method is capable of achieving higher registration accuracy than existing multimodal registration algorithms for situations, where little to no overlapping regions exist.