Medical Image Registration by Combining Gradient Vector Flow and Conditional Entropy Measure


KIPS Transactions on Software and Data Engineering, Vol. 17, No. 4, pp. 303-308, Apr. 2010
10.3745/KIPSTB.2010.17.4.303, Full Text:

Abstract

In this paper, we propose a medical image registration technique combining the gradient vector flow and modified conditional entropy. The registration is conducted by the use of a measure based on the entropy of conditional probabilities. To achieve the registration, we first define a modified conditional entropy (MCE) computed from the joint histograms for the area intensities of two given images. In order to combine the spatial information into a traditional registration measure, we use the gradient vector flow field. Then the MCE is computed from the gradient vector flow intensity (GVFI) combining the gradient information and their intensity values of original images. To evaluate the performance of the proposed registration method, we conduct experiments with our method as well as existing method based on the mutual information (MI) criteria. We evaluate the precision of MI- and MCE-based measurements by comparing the registration obtained from MR images and transformed CT images. The experimental results show that the proposed method is faster and more accurate than other optimization methods.


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Cite this article
[IEEE Style]
M. E. Lee, S. H. Kim, S. W. Kim and J. S. Lim, "Medical Image Registration by Combining Gradient Vector Flow and Conditional Entropy Measure," KIPS Journal B (2001 ~ 2012) , vol. 17, no. 4, pp. 303-308, 2010. DOI: 10.3745/KIPSTB.2010.17.4.303.

[ACM Style]
Myung Eun Lee, Soo Hyung Kim, Sun Worl Kim, and Jun Sik Lim. 2010. Medical Image Registration by Combining Gradient Vector Flow and Conditional Entropy Measure. KIPS Journal B (2001 ~ 2012) , 17, 4, (2010), 303-308. DOI: 10.3745/KIPSTB.2010.17.4.303.