Evaluation of Ridge Seeking Operators for Multimodality Medical Image Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Intensity- and Gradient-Based Stereo Matching Using Hierarchical Gaussian Basis Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D Multi-Modality Medical Image Registration Using Feature Space Clustering
CVRMed '95 Proceedings of the First International Conference on Computer Vision, Virtual Reality and Robotics in Medicine
Alignment by maximization of mutual information
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Point similarity measures for non-rigid registration of multi-modal data
Computer Vision and Image Understanding
Nonlinear registration using variational principle for mutual information
Pattern Recognition
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Hierarchical multimodal image registration based on adaptive local mutual information
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
IPCAI'12 Proceedings of the Third international conference on Information Processing in Computer-Assisted Interventions
Local Mutual Information for Dissimilarity-Based Image Segmentation
Journal of Mathematical Imaging and Vision
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We present a deformable registration algorithm for multi-modality images based on information theoretic similarity measures at the scale of individual image voxels. We derive analytical expressions for the mutual information, the joint entropy, and the sum of marginal entropies of two images over a small neighborhood in terms of image gradients. Using these expressions, we formulate image registration algorithms maximizing local similarity over the whole image domain in an energy minimization framework. This strategy produces highly elastic image alignment as the registration is driven by voxel similarities between the images, the algorithms are easily implementable using the closed-form expressions for the derivative of the optimization function with respect to the deformation, and avoid estimation of joint and marginal probability densities governing the image intensities essential to conventional information theoretic image registration methods.