QUANTIFYING DEFORMATION USING INFORMATION THEORY: THE LOG-UNBIASED NONLINEAR REGISTRATION
Yanovsky, I.; Ming-Chang Chiang; Thompson, P.M.; Klunder, A.D.; Becker, J.T.; Davis, S.W.; Toga, A.W.; Leow, A.D.
Biomedical Imaging: From Nano to Macro, 2007. ISBI 2007. 4th IEEE International Symposium on
Volume , Issue , 12-15 April 2007 Page(s):13 - 16
Digital Object Identifier 10.1109/ISBI.2007.356776
Summary: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
View citation and abstract |
Cart
