Image similarity using mutual information of regions

Published

Journal Article

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. © Springer-Verlag 2004.

Duke Authors

Cited Authors

  • Russakoff, DB; Tomasi, C; Rohlfing, T; Maurer, CR

Published Date

  • December 1, 2004

Published In

Volume / Issue

  • 3023 /

Start / End Page

  • 596 - 607

Electronic International Standard Serial Number (EISSN)

  • 1611-3349

International Standard Serial Number (ISSN)

  • 0302-9743

Citation Source

  • Scopus