Adaptive registration using local information measures

Rapidly advancing registration methods increasingly employ warping transforms. High degrees of freedom (DOF) warpings can be specified by manually placing control points or instantiating a regular, dense grid of control points everywhere. The former approach is laborious and prone to operator bias, whereas the latter is computationally expensive. We propose to improve upon the latter approach by adaptively placing control points where they are needed. Local estimates of mutual information (MI) and entropy are used to identify local regions requiring additional DOF.

[1]  Fred L. Bookstein,et al.  Morphometric Tools for Landmark Data. , 1998 .

[2]  Charles R. Meyer,et al.  Demonstration of accuracy and clinical versatility of mutual information for automatic multimodality image fusion using affine and thin-plate spline warped geometric deformations , 1997, Medical Image Anal..

[3]  D. Hill,et al.  Medical image registration , 2001, Physics in medicine and biology.

[4]  James S. Duncan,et al.  Medical Image Analysis , 1999, IEEE Pulse.

[5]  Chuck Meyer,et al.  Evaluation of Control Point Selection in Automatic, Mutual Information Driven, 3D Warping , 1998, MICCAI.

[6]  Hyunjin Park,et al.  Grid Refinement in Adaptive Non-rigid Registration , 2003, MICCAI.

[7]  Alfred O. Hero,et al.  Tree structured non-linear signal modeling and prediction , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.

[8]  Gary E. Christensen,et al.  Landmark and Intensity-Based, Consistent Thin-Plate Spline Image Registration , 2001, IPMI.

[9]  C R Meyer,et al.  Motion correction in fMRI via registration of individual slices into an anatomical volume , 1999, Magnetic resonance in medicine.

[10]  C R Meyer,et al.  Semiautomatic registration of volumetric ultrasound scans. , 1999, Ultrasound in medicine & biology.

[11]  A. Izenman.  Recent Developments in Nonparametric Density Estimation , 1991 .

[12]  C. R. Meyer,et al.  Warping Normal Patients onto the ICBM Atlas by Maximizing MI , 1998, NeuroImage.

[13]  Sung Yong Shin,et al.  Image Metamorphosis with Scattered Feature Constraints , 1996, IEEE Trans. Vis. Comput. Graph..

[14]  Gerald Q. Maguire,et al.  Comparison and evaluation of retrospective intermodality brain image registration techniques. , 1997, Journal of computer assisted tomography.

[15]  Haiying Liu,et al.  A Generic Framework for Non-rigid Registration Based on Non-uniform Multi-level Free-Form Deformations , 2001, MICCAI.

[16]  A Collignon,et al.  Automated multimodality image registration using information theory , 1995 .

[17]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[18]  K. Mardia,et al.  Statistical Shape Analysis , 1998 .

[19]  Torsten Rohlfing,et al.  Intensity-Based Non-rigid Registration Using Adaptive Multilevel Free-Form Deformation with an Incompressibility Constraint , 2001, MICCAI.

[20]  G. Marchal,et al.  Multi-modal volume registration by maximization of mutual information , 1997 .

[21]  Benoit M. Dawant,et al.  Adaptive free-form deformation for interpatient medical image registration , 2001, SPIE Medical Imaging.

[22]  A. Izenman.  Review Papers: Recent Developments in Nonparametric Density Estimation , 1991 .

[23]  Benoit M. Dawant,et al.  The adaptive bases algorithm for intensity-based nonrigid image registration , 2003, IEEE Transactions on Medical Imaging.

[24]  Alfred O. Hero,et al.  Tree-structured nonlinear signal modeling and prediction , 1999, IEEE Trans. Signal Process..

[25]  C. Meyer,et al.  Automated generation of a four-dimensional model of the liver using warping and mutual information. , 2003, Medical physics.