Universal Estimation of Information Measures for Analog Sources
Foundations and Trends in Communications and Information Theory
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Multiresolution image registration based on tree data structures
Graphical Models
MBIA'11 Proceedings of the First international conference on Multimodal brain image analysis
An information-theoretic method for multimodality medical image registration
Expert Systems with Applications: An International Journal
Graph-theoretic image alignment using topological features
CompIMAGE'10 Proceedings of the Second international conference on Computational Modeling of Objects Represented in Images
Multimodality image alignment using information-theoretic approach
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part II
Quad-tree based entropy estimator for fast and robust brain image registration
WBIR'12 Proceedings of the 5th international conference on Biomedical Image Registration
A generative model for probabilistic label fusion of multimodal data
MBIA'12 Proceedings of the Second international conference on Multimodal Brain Image Analysis
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We provide a detailed analysis of the use of minimal spanning graphs as an alignment method for registering multimodal images. This yields an efficient graph theoretic algorithm that, for the first time, jointly estimates both an alignment measure and a viable descent direction with respect to a parameterized class of spatial transformations. We also show how prior information about the interimage modality relationship from prealigned image pairs can be incorporated into the graph-based algorithm. A comparison of the graph theoretic alignment measure is provided with more traditional measures based on plug-in entropy estimators. This highlights previously unrecognized similarities between these two registration methods. Our analysis gives additional insight into the tradeoffs the graph-based algorithm is making and how these will manifest themselves in the registration algorithm's performance.