Image Similarity Using Mutual Information of Regions

  • Daniel B. Russakoff
  • Carlo Tomasi
  • Torsten Rohlfing
  • Calvin R. MaurerJr.
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3023)


Mutual information (MI) has emerged in recent years as an effective similarity measure for comparing images. One drawback of MI, however, is that it is calculated on a pixel by pixel basis, meaning that it takes into account only the relationships between corresponding individual pixels and not those of each pixel’s respective neighborhood. As a result, much of the spatial information inherent in images is not utilized. In this paper, we propose a novel extension to MI called regional mutual information (RMI). This extension efficiently takes neighborhood regions of corresponding pixels into account. We demonstrate the usefulness of RMI by applying it to a real-world problem in the medical domain—intensity-based 2D-3D registration of X-ray projection images (2D) to a CT image (3D). Using a gold-standard spine image data set, we show that RMI is a more robust similarity meaure for image registration than MI.


Mutual Information Independent Component Analysis Central Limit Theorem Image Registration Independent Component Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Daniel B. Russakoff
    • 1
    • 2
  • Carlo Tomasi
    • 3
  • Torsten Rohlfing
    • 2
  • Calvin R. MaurerJr.
    • 2
  1. 1.Department of Computer ScienceStanford UniversityStanfordUSA
  2. 2.Image Guidance LaboratoriesStanford UniversityStanfordUSA
  3. 3.Department of Computer ScienceDuke UniversityDurhamUSA

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