Extension of the ICP Algorithm to Nonrigid Intensity-Based Registration of 3D Volumes

We present in this paper a new registration and gain correction algorithm for 3D medical images. It is intensity based. The basic idea is to represent images by 4D points (xj,yj,zj,ij) and to define a global energy function based on this representation. For minimization, we propose a technique which does not require computing the derivatives of this criterion with respect to the parameters. It can be understood as an extension of the Iterative Closest Point algorithm or as an application of the formalism proposed by L. Cohen (Proceedings of the Fifth International Conference on Computer Vision (ICCV '95), Boston, June 1995). Two parameters enable us to develop a coarse-to-fine strategy both for resolution and for deformation. Our technique presents the advantage of minimizing a well-defined global criterion, to deal with various classes of transformations (for example, rigid, affine, volume spline, and radial basis functions), 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 our approach. We also explain how one can compute basic statistics on the deformation parameters to constrain the set of possible deformations by learning and to discriminate between different groups.

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

[2]  George Henry Dunteman,et al.  Introduction To Multivariate Analysis , 1984 .

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

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

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

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

[7]  Gerald Farin,et al.  Curves and surfaces for computer aided geometric design , 1990 .

[8]  T. Crow.  Temporal lobe asymmetries as the key to the etiology of schizophrenia. , 1990, Schizophrenia bulletin.

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

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

[11]  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.

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

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

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

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

[16]  J. Alger.  The 1991 Nobel Prize in chemistry awarded to an MRI investigator. , 1992, Journal of computer assisted tomography.

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

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

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

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

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

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

[23]  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.

[24]  Karl J. Friston,et al.  Statistical parametric maps in functional imaging: A general linear approach , 1994 .

[25]  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.

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

[27]  Nicholas Ayache,et al.  3D-2D projective registration of free-form curves and surfaces , 1995, Proceedings of IEEE International Conference on Computer Vision.

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

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

[30]  Jinah Park,et al.  Volumetric deformable models with parameter functions: A new approach to the 3D motion analysis of the LV from MRI-SPAMM , 1995, Proceedings of IEEE International Conference on Computer Vision.

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

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

[33]  François G. Meyer,et al.  TRACKING MYOCARDIAL DEFORMATION USING SPATIALLY-CONSTRAINED VELOCITIES , 1995 .

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

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

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

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

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

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

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

[41]  Guy Marchal,et al.  Automated multi-moda lity image registration based on information theory , 1995 .

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

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

[44]  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.

[45]  Paul M. Thompson,et al.  A surface-based technique for warping three-dimensional images of the brain , 1996, IEEE Trans. Medical Imaging.

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

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

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

[49]  Nicholas Ayache,et al.  Definition of a 4D continuous polar transformation for the tracking and the analysis of LV motion , 1997, CVRMed.

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