Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
A survey of image registration techniques
ACM Computing Surveys (CSUR)
Alignment by maximization of mutual information
Alignment by maximization of mutual information
Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis
Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis
Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)
On Effective Methods for Implicit Piecewise Smooth Surface Recovery
SIAM Journal on Scientific Computing
Robust Multimodal Registration Using Local Phase-Coherence Representations
Journal of Signal Processing Systems
Image Fusion for Enhanced Visualization: A Variational Approach
International Journal of Computer Vision
Cervical Vertebrae Tracking in Video-Fluoroscopy Using the Normalized Gradient Field
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
An adaptive Monte Carlo approach to phase-based multimodal image registration
IEEE Transactions on Information Technology in Biomedicine
Registration-based propagation for whole heart segmentation from compounded 3D echocardiography
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Feature-Driven Direct Non-Rigid Image Registration
International Journal of Computer Vision
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A particular problem in image registration arises for multi-modal images taken from different imaging devices and/or modalities. Starting in 1995, mutual information has shown to be a very successful distance measure for multi-modal image registration. However, mutual information has also a number of well-known drawbacks. Its main disadvantage is that it is known to be highly non-convex and has typically many local maxima. This observation motivate us to seek a different image similarity measure which is better suited for optimization but as well capable to handle multi-modal images. In this work we investigate an alternative distance measure which is based on normalized gradients and compare its performance to Mutual Information. We call the new distance measure Normalized Gradient Fields (NGF).