Topology Preserving Log-Unbiased Nonlinear Image Registration: Theory and Implementation

In this paper, we present a novel framework for constructing large deformation log-unbiased image registration models that generate theoretically and intuitively correct deformation maps. Such registration models do not rely on regridding and are inherently topology preserving. We apply information theory to quantify the magnitude of deformations and examine the statistical distributions of Jacobian maps in the logarithmic space. To demonstrate the power of the proposed framework, we generalize the well known viscous fluid registration model to compute logunbiased deformations. We tested the proposed method using a pair of binary corpus callosum images, a pair of two-dimensional serial MRI images, and a set of threedimensional serial MRI brain 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 maps of local tissue change.

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Igor Yanovsky, Paul M. Thompson, Stanley Osher, Alex D. Leow
• Keywords: Computer Vision, Correct Deformation Maps, CVPR 2007, Fluid Registration Method, Serial Mri Images, Threedimensional Serial Mri, Viscous Fluid Registration