Coupling dense and landmark-based approaches for nonrigid registration

We investigate the introduction of cortical constraints for non rigid intersubject brain registration. We extract sulcal patterns with the active ribbon method, presented by Le Goualher et al. (1997). An energy based registration method (Hellier et al., 2001), which will be called photometric registration method in this paper, makes it possible to incorporate the matching of cortical sulci. The local sparse similarity and the photometric similarity are, thus, expressed in a unified framework. We show the benefits of cortical constraints on a database of 18 subjects, with global and local assessment of the registration. This new registration scheme has also been evaluated on functional magnetoencephalography data. We show that the anatomically constrained registration leads to a substantial reduction of the intersubject functional variability.

[1]  Robert T. Schultz,et al.  Registration of Cortical Anatomical Structures via Robust 3D Point Matching , 1999, IPMI.

[2]  Alex Pentland,et al.  Characterization of Neuropathological Shape Deformations , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

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

[4]  Alan C. Evans,et al.  An MRI-based stereotactic atlas from 250 young normal subjects , 1992 .

[5]  D. Louis Collins,et al.  Cortical Constraints for Non-Linear Cortical Registration , 1996, VBC.

[6]  Alexis Gourdon,et al.  The Marching lines algorithm : new results and proofs , 1993 .

[7]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[8]  D. Louis Collins,et al.  Automated extraction and variability analysis of sulcal neuroanatomy , 1999, IEEE Transactions on Medical Imaging.

[9]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[10]  D. Louis Collins,et al.  Animal: Validation and Applications of Nonlinear Registration-Based Segmentation , 1997, Int. J. Pattern Recognit. Artif. Intell..

[11]  David Metcalf,et al.  A Digital Brain Atlas for Surgical Planning, Model-Driven Segmentation, and Teaching , 1996, IEEE Trans. Vis. Comput. Graph..

[12]  Patrick Pérez,et al.  An energy-based framework for dense 3D registration of volumetric brain images , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[13]  Jürgen Weese,et al.  Point-Based Elastic Registration of Medical Image Data Using Approximating Thin-Plate Splines , 1996, VBC.

[14]  Christos Davatzikos,et al.  Mapping the Cerebral Sulci: Application to Morphological Analysis of the Cortex and to Non-rigid Registration , 1997, IPMI.

[15]  Alex Pentland,et al.  Closed-form solutions for physically-based shape modeling and recognition , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[16]  Michael J. Black,et al.  On the unification of line processes, outlier rejection, and robust statistics with applications in early vision , 1996, International Journal of Computer Vision.

[17]  James C. Gee,et al.  Effect of spatial normalization on analysis of functional data , 1997, Medical Imaging.

[18]  D. Schwartz,et al.  Evaluation of a New MEG-EEG Spatio-Temporal Localization Approach Using a Realistic Source Model , 2004, Brain Topography.

[19]  D. Louis Collins,et al.  Automatic Identification of Cortical Sulci Using a 3D Probabilistic Atlas , 1998, MICCAI.

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

[21]  Christopher J. Taylor,et al.  3D point distribution models of the cortical sulci , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[22]  Karl Rohr,et al.  Parameter-Free Elastic Deformation Approach for 2D and 3D Registration Using Prescribed Displacements , 1999, Journal of Mathematical Imaging and Vision.

[23]  Richard M. Leahy,et al.  Surface-based labeling of cortical anatomy using a deformable atlas , 1997, IEEE Transactions on Medical Imaging.

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

[25]  M. Musen,et al.  Handbook of Medical Informatics , 2002 .

[26]  Gábor Székely,et al.  Mapping the human cerebral cortex using 3-D medial manifolds , 1992, Other Conferences.

[27]  Arthur W. Toga,et al.  A Probabilistic Atlas of the Human Brain: Theory and Rationale for Its Development The International Consortium for Brain Mapping (ICBM) , 1995, NeuroImage.

[28]  Christian Barillot,et al.  Modeling Cortical Sulci with Active Ribbons , 1997, Int. J. Pattern Recognit. Artif. Intell..

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

[30]  C. Gorman,et al.  PI , 2021, Encyclopedic Dictionary of Archaeology.

[31]  Alan C. Evans,et al.  Statistical Sulcal Shape Comparisons: Application to the Detection of Genetic Encoding of the Central Sulcus Shape , 2000, NeuroImage.

[32]  Fabrice Heitz,et al.  3D deformable image matching using multiscale minimization of global energy functions , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[33]  Christian Barillot,et al.  Brain tissue classification from MRI data by means of texture analysis , 1992, Medical Imaging.

[34]  Jean-Francois Mangin,et al.  Multisubject Non-rigid Registration of Brain MRI Using Intensity and Geometric Features , 2001, MICCAI.

[35]  Christos Davatzikos,et al.  Spatial Transformation and Registration of Brain Images Using Elastically Deformable Models , 1997, Comput. Vis. Image Underst..

[36]  Alexis Gourdon,et al.  Computing the Differential Characteristics of Isointensity Surfaces , 1995, Comput. Vis. Image Underst..

[37]  Isabelle Bloch,et al.  From 3D magnetic resonance images to structural representations of the cortex topography using topology preserving deformations , 1995, Journal of Mathematical Imaging and Vision.

[38]  D. V. von Keyserlingk,et al.  A quantitative approach to spatial variation of human cerebral sulci. , 1988, Acta anatomica.

[39]  Jean-Francois Mangin,et al.  Automatic Recognition of Cortical Sulci Using a Congregation of Neural Networks , 2000, MICCAI.

[40]  Gary E. Christensen,et al.  Deformable Shape Models for Anatomy , 1994 .

[41]  Robert T. Schultz,et al.  A New Approach to 3D Sulcal Ribbon Finding from MR Images , 1999, MICCAI.

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

[43]  Morten Bro-Nielsen,et al.  Fast Fluid Registration of Medical Images , 1996, VBC.

[44]  Edgar M. Housepian Atlas d'anatomie stereotaxique du telencephale. , 1968 .

[45]  D. Louis Collins,et al.  Retrospective evaluation of intersubject brain registration , 2003, IEEE Transactions on Medical Imaging.

[46]  C. Barillot,et al.  Registration of MEG/EEG data with 3D MRI: Methodology and precision issues , 1996, Brain Topography.

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

[48]  R. Woods,et al.  Mathematical/computational challenges in creating deformable and probabilistic atlases of the human brain , 2000, Human brain mapping.

[49]  M. Revenu,et al.  Detection and identification of Sulci on 3D MRI , 1997 .

[50]  J. Valk,et al.  Referentially oriented cerebral MRI anatomy , 1994 .

[51]  M. Nagao,et al.  Edge preserving smoothing , 1979 .

[52]  Pierre Hellier,et al.  Hierarchical estimation of a dense deformation field for 3-D robust registration , 2001, IEEE Transactions on Medical Imaging.

[53]  Jean-Francois Mangin,et al.  Automatic recognition of cortical sulci of the human brain using a congregation of neural networks , 2002, Medical Image Anal..

[54]  Max A. Viergever,et al.  Interpolation Artefacts in Mutual Information-Based Image Registration , 2000, Comput. Vis. Image Underst..

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

[56]  Max A. Viergever,et al.  Scale and the differential structure of images , 1992, Image Vis. Comput..

[57]  Marinette Revenu,et al.  Morphometry and Identification of Brain Sulci on Three-Dimensional MR Images , 1995 .

[58]  M. Torrens Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268 , 1990 .

[59]  Christopher J. Taylor,et al.  Using Local Geometry to Build 3D Sulcal Models , 1999, IPMI.

[60]  Michael I. Miller,et al.  Volumetric transformation of brain anatomy , 1997, IEEE Transactions on Medical Imaging.

[61]  J. Gee Probabilistic matching of deformed images , 1996 .

[62]  Christos Davatzikos,et al.  Hierarchical Matching of Cortical Features for Deformable Brain Image Registration , 1999, IPMI.

[63]  D. Louis Collins,et al.  Non-linear Cerebral Registration with Sulcal Constraints , 1998, MICCAI.