A Unified Statistical and Information Theoretic Framework for Multi-modal Image Registration

Abstract
We formulate and interpret several multi-modal 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.

Publications

L . Zöllei, J. Fisher, W.M. Wells III: "A Unified Statistical and Information Theoretic Framework for Multi-modal Image Registration",
Information Processing in Medical Imaging (IPMI) 2003, LNCS 2732, pp. 366-377.

Researchers

Lilla Zöllei                     lzollei at csail.mit.edu

John Fisher                    fisher at csail.mit.edu

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