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

Inf Process Med Imaging. 2003 Jul;18:366-77. doi: 10.1007/978-3-540-45087-0_31.

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.

Publication types

  • Comparative Study
  • Evaluation Study
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, U.S. Gov't, P.H.S.
  • Validation Study

MeSH terms

  • Algorithms*
  • Brain / anatomy & histology*
  • Brain / diagnostic imaging*
  • Computational Biology / methods
  • Computer Simulation
  • Humans
  • Image Enhancement / methods
  • Image Interpretation, Computer-Assisted / methods*
  • Imaging, Three-Dimensional / methods
  • Information Theory
  • Likelihood Functions
  • Models, Biological*
  • Models, Statistical
  • Pattern Recognition, Automated*
  • Radiography
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Subtraction Technique*