Sliding Geometries in Deformable Image Registration

Regularization is used in deformable image registration to encourage plausible displacement fields, and significantly impacts the derived correspondences. Sliding motion, such as that between the lungs and chest wall and between the abdominal organs, complicates registration because many regularizations are global smoothness constraints that produce errors at object boundaries. We present locally adaptive regularizations that handle sliding objects with locally planar and tubular geometries. These regularizations allow discontinuities to develop in the displacement field at sliding interfaces and increase the independence with which regions surrounding distinct geometric structures can behave. Validation is performed by registering inhale and exhale abdominal computed tomography (CT) images and artificial images of a sliding tube. The sliding registration methods produce more realistic correspondences that may better reflect the underlying physical motion, while performing as well as the diffusive regularization with respect to image match.

[1]  Heinz Handels,et al.  Slipping Objects in Image Registration: Improved Motion Field Estimation with Direction-Dependent Regularization , 2009, MICCAI.

[2]  Nassir Navab,et al.  Medical Image Computing and Computer-Assisted Intervention - MICCAI 2010, 13th International Conference, Beijing, China, September 20-24, 2010, Proceedings, Part III , 2010, MICCAI.

[3]  M. Brady,et al.  Discontinuity preserving regularisation for variational optical-flow registration using the modified L p norm , 2010 .

[4]  Hans Knutsson,et al.  Adaptive anisotropic regularization of deformation fields for non-rigid registration using the morphon framework , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[5]  Nicholas Ayache,et al.  Grid powered nonlinear image registration with locally adaptive regularization , 2004, Medical Image Anal..

[6]  Stephen R. Aylward,et al.  Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction , 2002, IEEE Transactions on Medical Imaging.

[7]  Christopher J. Taylor,et al.  Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009 , 2009, Lecture Notes in Computer Science.

[8]  Hua Yang,et al.  Deformable image registration of sliding organs using anisotropic diffusive regularization , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[9]  Nathan D. Cahill,et al.  A Demons Algorithm for Image Registration with Locally Adaptive Regularization , 2009, MICCAI.

[10]  Jan Modersitzki,et al.  Numerical Methods for Image Registration , 2004 .

[11]  Jeffrey A. Fessler,et al.  Discriminative sliding preserving regularization in medical image registration , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[12]  Eric A. Hoffman,et al.  Lung Lobar Slippage Assessed with the Aid of Image Registration , 2010, MICCAI.