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QUANTIFYING DEFORMATION USING INFORMATION THEORY: THE LOG-UNBIASED NONLINEAR REGISTRATION

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8 Author(s)
Yanovsky, I. ; Dept. of Math., California Univ., Los Angeles, CA ; Ming-Chang Chiang ; Thompson, P.M. ; Klunder, A.D.
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In the past decade, information theory has been studied extensively in medical imaging. In particular, maximization of mutual information has been shown to yield good results in multi-modal image registration. In this paper, we apply information theory to quantifying the magnitude of deformations. We examine the statistical distributions of Jacobian maps in the logarithmic space, and develop a new framework for constructing image registration methods. The proposed framework yields both theoretically and intuitively correct deformation maps, and is compatible with large-deformation models. In the results section, we tested the proposed method using a pair of serial MRI images. We compared our results to those computed using the viscous fluid registration method, and demonstrated that the proposed method is advantageous when recovering voxel-wise local tissue change

Published in:

Biomedical Imaging: From Nano to Macro, 2007. ISBI 2007. 4th IEEE International Symposium on

Date of Conference:

12-15 April 2007