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A unified statistical and information theoretic framework for multi-modal image registration.

Lilla Zöllei·John W Fisher·William M Wells
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Abstract

We formulate and interpret several registration methods in the context of a unified statistical and information theoretic framework. A unified interpretation clarifies the implicit assumptions of each method yielding a better understanding of their relative strengths and weaknesses. Additionally, we discuss a generative statistical model from which we derive a novel analysis tool, the auto-information function, as a means of assessing and exploiting the common spatial dependencies inherent in multi-modal imagery. We analytically derive useful properties of the auto-information as well as verify them empirically on multi-modal imagery. Among the useful aspects of the auto-information function is that it can be computed from imaging modalities independently and it allows one to decompose the search space of registration problems.

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AlgorithmsBrainComputational BiologyComputer SimulationHumansImage EnhancementImage Interpretation, Computer-AssistedImaging, Three-DimensionalInformation TheoryLikelihood FunctionsModels, BiologicalModels, StatisticalPattern Recognition, AutomatedRadiographyReproducibility of ResultsSensitivity and SpecificitySubtraction TechniqueComparative StudyEvaluation StudyJournal ArticleResearch Support, Non-U.S. Gov'tResearch Support, U.S. Gov't, Non-P.H.S.Research Support, U.S. Gov't, P.H.S.Validation Study

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BibTeXRIS

Lilla Zöllei, John W Fisher, William M Wells. 2003. A unified statistical and information theoretic framework for multi-modal image registration.. https://doi.org/10.1007/978-3-540-45087-0_31

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