Image Similarity Using Mutual Information of Regions

  • Russakoff D
  • Tomasi C
  • Rohlfing T
 et al. 
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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.

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  • Computer Science

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  • Daniel Russakoff

  • Carlo Tomasi

  • Torsten Rohlfing

  • Calvin Maurer

  • Tomáš Pajdla

  • Jirí Matas

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