Extension of the ICP algorithm to non-rigid intensity-based registration of 3D volumes

Presents a new registration and gain correction algorithm for 3D medical images. It is intensity based. The basic idea is to represent the images by 4D points (x/sub j/, y/sub j/, z/sub j/, i/sub j/) and to define a global energy function based on this representation. For minimization, the authors propose a technique which does not require to compute the derivatives of this criterion with respect to the parameters. It can be understood as an extension of the Iterative Closest Point algorithm (P. Besl and N. McKay, 1992; Z. Zhang, 1992) or as an application of the formalism proposed by L. Cohen (Use of auxiliary variables in computer vision problems. In Proceedings of the Fifth International Conference on Computer Vision (ICCV '95), Boston, June 1995). Two parameters allow one to have a coarse to fine strategy both for resolution and deformation. The authors' technique presents the advantage to minimize a well defined global criterion to deal with various classes of transformations (for example rigid, affine and volume spline), to be simple to implement and to be efficient in practice. Results on real brain and heart 3D images are presented to demonstrate the validity of the authors' approach.

[1]  J. Declerck,et al.  Automatic registration and alignment on a template of cardiac stress and rest SPECT images , 1996, Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis.

[2]  G. Subsol,et al.  Construction automatique d'atlas anatomiques morphométriques à partir d'images médicales tridimensionnelles : application à un atlas du crâne , 1995 .

[3]  Paul A. Viola.  Alignment by maximisation of mutual information , 1993 .

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

[5]  Gérard G. Medioni,et al.  Object modelling by registration of multiple range images , 1992, Image Vis. Comput..

[6]  W. Eric L. Grimson,et al.  Adaptive Segmentation of MRI Data , 1995, CVRMed.

[7]  Lisa M. Brown,et al.  A survey of image registration techniques , 1992, CSUR.

[8]  Isabelle Bloch,et al.  Fast Nonsupervised 3D Registration of PET and MR Images of the Brain , 1994, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[9]  J. Mazziotta,et al.  Rapid Automated Algorithm for Aligning and Reslicing PET Images , 1992, Journal of computer assisted tomography.

[10]  Laurent D. Cohen.  Auxiliary variables for deformable models , 1995, Proceedings of IEEE International Conference on Computer Vision.

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

[12]  R. Szeliski.  Matching 3-D Anatomical Surfaces with Non-Rigid Volumetric Deformations , 1994 .

[13]  Karl J. Friston,et al.  Spatial registration and normalization of images , 1995 .

[14]  Nicholas Ayache,et al.  3D-2D Projective Registration of Free-Form Curves and Surfaces , 1997, Comput. Vis. Image Underst..

[15]  M. Viergever,et al.  Medical image matching-a review with classification , 1993, IEEE Engineering in Medicine and Biology Magazine.

[16]  G. Malandain.  Filtrage, topologie et mise en correspondance d'images médicales multidimensionnelles , 1992 .

[17]  Michael I. Miller,et al.  Hierarchical brain mapping via a generalized Dirichlet solution for mapping brain manifolds , 1995, Optics & Photonics.

[18]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Brian Everitt,et al.  Principles of Multivariate Analysis , 2001 .

[20]  Guido Gerig,et al.  Segmentation of 3D Objects from MRI Volume Data Using Constrained Elastic Deformations of Flexible Fourier Surface Models , 1995, CVRMed.

[21]  A. J. Collins,et al.  Introduction To Multivariate Analysis , 1981 .

[22]  Jean-Philippe Thirion,et al.  Fast Non-Rigid Matching of 3D Medical Images , 1995 .

[23]  Christos Davatzikos.  Nonlinear registration of brain images using deformable models , 1996, Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis.

[24]  Nur Arad,et al.  Image Warping Using Few Anchor Points and Radial Functions , 1995, Comput. Graph. Forum.

[25]  G. Champleboux,et al.  From accurate range imaging sensor calibration to accurate model-based 3D object localization , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[26]  Hong-Tzong Yau,et al.  Automated precision measurement of surface profile in CAD-directed inspection , 1992, IEEE Trans. Robotics Autom..

[27]  James C. Gee,et al.  Probabilistic Matching of Brain Images , 1995 .

[28]  Nicholas Ayache,et al.  Medical computer vision, virtual reality and robotics , 1995, Image Vis. Comput..

[29]  W. Eric L. Grimson,et al.  Adaptive Segmentation of MRI Data , 1995, CVRMed.

[30]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[31]  Gérard G. Medioni,et al.  Object modeling by registration of multiple range images , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[32]  Alex Pentland,et al.  Generalized Image Matching: Statistical Learning of Physically-Based Deformations , 1996, ECCV.

[33]  Derek L. G. Hill,et al.  Craniotomy simulation and guidance using a stereo video based tracking system (VISLAN) , 1994, Other Conferences.

[34]  E. McVeigh.  MRI of myocardial function: motion tracking techniques. , 1996, Magnetic resonance imaging.

[35]  M I Miller,et al.  Mathematical textbook of deformable neuroanatomies. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[36]  Gerald E. Farin,et al.  Curves and surfaces for computer-aided geometric design - a practical guide, 4th Edition , 1997, Computer science and scientific computing.

[37]  Michael Ian Shamos,et al.  Computational geometry: an introduction , 1985 .

[38]  Nicholas Ayache,et al.  Automatic Retrieval of Anatomical Structures in 3D Medical Images , 1995, CVRMed.

[39]  Timothy F. Cootes,et al.  Medical image interpretation using active shape models:Recent advances. , 1995 .

[40]  Fred L. Bookstein,et al.  Principal Warps: Thin-Plate Splines and the Decomposition of Deformations , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[41]  W. Eric L. Grimson,et al.  An automatic registration method for frameless stereotaxy, image guided surgery, and enhanced reality visualization , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[42]  Nicholas Ayache,et al.  Randomness and geometric features in computer vision , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.