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Proceedings Paper

Mathematical properties of information theoretic image similarity measures
Author(s): Oskar Škrinjar
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Paper Abstract

Joint entropy, mutual information, and normalized mutual information are widely used image similarity measures in multimodality image registration and other problems that involve comparing images with arbitrary intensity relationships. While these image similarity measures have been successfully used in various applications, their mathematical properties have not been studied thoroughly. This paper analyzes several properties of practical interest of the three image similarity measures. It is shown that mutual information, despite its popularity, and joint entropy have a few undesirable properties. On the other hand, normalized mutual information does not suffer from these problems. The properties are proven mathematically, which renders the conclusions independent of image type, noise, and artifacts. The conclusions are in line with the results of previous experimental studies, in which normalized mutual information outperformed other information theoretic image similarity measures.

Paper Details

Date Published: 10 March 2006
PDF: 7 pages
Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 614433 (10 March 2006); doi: 10.1117/12.654238
Show Author Affiliations
Oskar Škrinjar, Georgia Institute of Technology (United States)


Published in SPIE Proceedings Vol. 6144:
Medical Imaging 2006: Image Processing
Joseph M. Reinhardt; Josien P. W. Pluim, Editor(s)

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