A level-set based approach to image registration

Image registration is an often encountered problem in various fields including medical imaging, computer vision and image processing. Here, the authors present a novel curve evolution approach expressed in a level-set framework to achieve image registration. The key features of the authors' algorithm are, (a) it can account for small and large deformations, (b) it is very fast (unlike most large deformation schemes) and (c) existence and uniqueness of the solution for the evolution model has been established in a Sobolev space. The authors demonstrate the implementation results on a variety of images including synthetic and real data.

[1]  O. Ladyženskaja.  Linear and Quasilinear Equations of Parabolic Type , 1968 .

[2]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[3]  M. Gage,et al.  The Curve Shortening Flow , 1987 .

[4]  S. Osher,et al.  Algorithms Based on Hamilton-Jacobi Formulations , 1988 .

[5]  Ruzena Bajcsy,et al.  Multiresolution elastic matching , 1989, Comput. Vis. Graph. Image Process..

[6]  P. Lions,et al.  Image selective smoothing and edge detection by nonlinear diffusion. II , 1992 .

[7]  Richard Szeliski,et al.  Hierarchical spline-based image registration , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Anthony J. Yezzi,et al.  Gradient flows and geometric active contour models , 1995, Proceedings of IEEE International Conference on Computer Vision.

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

[10]  Baba C. Vemuri,et al.  Shape Modeling with Front Propagation: A Level Set Approach , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Gary J. Balas,et al.  Optical flow: a curve evolution approach , 1996, IEEE Trans. Image Process..

[12]  Karl Zilles,et al.  A New Approach to Fast Elastic Alignment with Applications to Human Brain , 1996, VBC.

[13]  Michael I. Miller,et al.  Individualizing Neuroanatomic Atlases Using a Massively Parallel Computer , 1996, Computer.

[14]  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..

[15]  Joachim Weickert,et al.  A Review of Nonlinear Diffusion Filtering , 1997, Scale-Space.

[16]  Joachim Weickert.  A Review of Nonlinear Diiusion Filtering , 1997 .

[17]  Lawrence H. Staib,et al.  Elastic Model Based Non-rigid Registration Incorporation Statistical Shape Information , 1998, MICCAI.

[18]  Sartaj Sahni,et al.  An efficient motion estimator with application to medical image registration , 1998, Medical Image Anal..

[19]  Max A. Viergever,et al.  A survey of medical image registration , 1998, Medical Image Anal..

[20]  Jean-Philippe Thirion,et al.  Image matching as a diffusion process: an analogy with Maxwell's demons , 1998, Medical Image Anal..

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