An energy-based framework for dense 3D registration of volumetric brain images

In this paper we describe a new method for medical image registration. The registration is formulated as a minimization problem involving robust estimators. We propose an efficient hierarchical optimization framework which is both multiresolution and multigrid. An anatomical segmentation of the cortex is introduced in the adaptive partitioning of the volume on which the multigrid minimization is based. This allows to limit the estimation to the areas of interest, to accelerate the algorithm, and to refine the estimation in specified areas. Furthermore we introduce a methodology to constrain the registration with landmarks such as anatomical structures. The performances of this method are objectively evaluated on simulated data and its benefits are demonstrated on a large database of real acquisitions.

[1]  Patrick Pérez,et al.  A multigrid approach for hierarchical motion estimation , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[2]  Michael J. Black,et al.  On the unification of line processes , 1996 .

[3]  Patrick Pérez,et al.  Medical Image Registration with Robust Multigrid Techniques , 1999, MICCAI.

[4]  小野 道夫,et al.  Atlas of the Cerebral Sulci , 1990 .

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

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

[7]  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).

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

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

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

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

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

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

[15]  D. Louis Collins,et al.  Design and construction of a realistic digital brain phantom , 1998, IEEE Transactions on Medical Imaging.

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

[17]  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.

[18]  Wilfried Enkelmann,et al.  Investigations of multigrid algorithms for the estimation of optical flow fields in image sequences , 1988, Comput. Vis. Graph. Image Process..

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

[20]  Gary E. Christensen,et al.  Consistent Linear-Elastic Transformations for Image Matching , 1999, IPMI.

[21]  Nicholas Ayache,et al.  A General Scheme for Automatically Building 3D Morphometric Anatomical Atlases: application to a Sku , 1995 .

[22]  Demetri Terzopoulos,et al.  Image Analysis Using Multigrid Relaxation Methods , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.