Image Similarity Using Mutual Information of Regions

  • Russakoff D
  • Tomasi C
  • Rohlfing T
 et al. 
  • 53

    Readers

    Mendeley users who have this article in their library.
  • N/A

    Citations

    Citations of this article.

Abstract

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.

Author-supplied keywords

  • Computer Science

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Authors

  • Daniel Russakoff

  • Carlo Tomasi

  • Torsten Rohlfing

  • Calvin Maurer

  • Tomáš Pajdla

  • Jirí Matas

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free