Skip to main content

Cortical constraints for non-linear cortical registration

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1131))

Abstract

Correspondence of cortical structures is difficult to establish in automatic voxel-based non-linear registration of human brains based solely on gradient magnitude images. Experiments with a set of 9 real MRI data volumes demonstrates that the inclusion of 1) L vv-based features or 2) automatically extracted sulci significantly reduce overlap errors for gyri and sulci on the cortex. Mismatch between gyri can be addressed using manually labelled sulci.

We gratefully acknowledge post-doctoral funding from the Human Frontier Science Project Organization (DLC). We also acknowledge Positron Imaging Laboratory of the Montreal Neurological Institute for providing the stereotaxic MRI model and also the MR division of the Department of Radiology for providing the individual MR volumetric data used to test the algorithm. Parts of this project was funded by Medical Research Council (MRC SP-30) and an ICBM grant.

This is a preview of subscription content, log in via an institution.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. R. Bajcsy and S. Kovacic. Multiresolution elastic matching. Computer Vision, Graphics, and Image Processing, 46:1–21, 1989.

    Google Scholar 

  2. F. Bookstein. Thin-plate splines and the atlas problem for biomedical images. In A. Colchester and D. Hawkes, editors, Information Processing in Medical Imaging, Volume 511 of Lecture Notes in Computer Science, pages 326–342, Wye, UK, July 1991. IPMI, Springer-Verlag.

    Google Scholar 

  3. G. Christensen, R. Rabbitt, and M. Miller. 3d brain mapping using a deformable neuroanatomy. Physics in Med and Biol, 39:609–618, 1994.

    Google Scholar 

  4. D. Collins, C. Holmes, T. Peters, and A. Evans. Automatic 3D model-based neuroanatomical segmentation. Human Brain Mapping, 3(3):190–208, 1996.

    Google Scholar 

  5. D. L. Collins, T. M. Peters, and A. C. Evans. An automated 3D non-linear image deformation procedure for determination of gross morphometric variability in human brain. In Proceedings of Conference on Visualization in Biomedical Computing. SPIE, 1994.

    Google Scholar 

  6. D. Dean, P. Buckley, F. Bookstein, J. Kamath, and D. Kwon. Three dimensional mr-based morphometric comparison of schizophrenic and normal cerebral ventricles. In Proceedings of Conference on Visualization in Biomedical Computing, Lecture Notes in Computer Science, page this volume. Springer-Verlag, Sept. 1996.

    Google Scholar 

  7. A. C. Evans, W. Dai, D. L. Collins, P. Neelin, and T. Marrett. Warping of a computerized 3-D atlas to match brain image volumes for quantitative neuroanatomical and functional analysis. In Proceedings of the International Society of Optical Engineering: Medical Imaging V, volume 1445, San Jose, California, 27 February–1 March 1991. SPIE.

    Google Scholar 

  8. A. C. Evans, S. Marrett, J. Torrescorzo, S. Ku, and L. Collins. MRI-PET correlation in three dimensions using a volume-of-interest (VOI) atlas. Journal of Cerebral Blood Flow and Metabolism, 11(2):A69–78, Mar 1991.

    Google Scholar 

  9. L. M. J. Florack, B. M. ter Haar Romeny, J. J. Koenderink, and M. A. Viergever. Scale and the differential structure of images. Image and Vison Computing, 10:376–388, 1992.

    Google Scholar 

  10. P. T. Fox, M. A. Mintun, E. M. Reiman, and M. E. Raichle. Enhanced detection of focal brain responses using intersubject averaging and change-distribution analysis of subtracted PET images. Journal of Cerebral Blood Flow and Metabolism, 8:642–653, 1988.

    Google Scholar 

  11. K. Fristen, C. Frith, P. Liddle, and R. Frackowiak. Plastic transformation of PET images. Journal of Computer Assisted Tomography, 15(1):634–639, 1991.

    Google Scholar 

  12. N. A. G. Subsol, J.-P. Thirion. Application of an automatically built 3d morphometric brain atlas: study of cerebral ventricle shape. In Proceedings of Conference on Visualization in Biomedical Computing, Lecture Notes in Computer Science, page this volume. Springer-Verlag, Sept. 1996.

    Google Scholar 

  13. Y. Ge, J. Fitzpatrick, R. Kessler, and R. Margolin. Intersubject brain image registration using both cortical and subcortical landmarks. In Proceedings of SPIE Medical Imaging, volume 2434, pages 81–95. SPIE, 1995.

    Google Scholar 

  14. J. Gee, L. LeBriquer, and C. Barillot. Probabilistic matching of brain images. In Y. Bizais and C. Barillot, editors, Information Processing in Medical Imaging, Ile Berder, France, July 1995. IPMI, Kluwer.

    Google Scholar 

  15. G. L. Goualher, C. Barillot, Y. Bizais, and J.-M. Scarabin. Three-dimensional segmentation of cortical sulci using active models. In SPIE Medical Imaging, page in press, Newport-Beach, Calif., 1996. SPIE.

    Google Scholar 

  16. F. Lachmann and C. Barillot. Brain tissue classification from mri by means of texture analysis. In SPIE Medical Imaging VI, volume 1652, pages 72–83, Newport-Beach, Calif., 1992. SPIE.

    Google Scholar 

  17. F. Leitner, I. Marque, S. Lavalee, and P. Cinquin. Dynamic segmentation: finding the edge with snake splines. In Int. Conf. on Curves and Surfaces, pages 279–284. Academic Press, June 1991.

    Google Scholar 

  18. S. Luo and A. Evans. Matching sulci in 3d space using force-based deformation. IEEE Transactions on Medical Imaging, submitted Nov., 1994.

    Google Scholar 

  19. S. Sandor and R. Leahy. Towards automated labelling of the cerebral cortex using a deformable atlas. In Y. Bizais, C. Barillot, and R. DiPaola, editors, Information Processing in Medical Imaging, pages 127–138, Brest, France, Aug 1995. IPMI, Kluwer.

    Google Scholar 

  20. B. tar Haar Romeny, L. M. Florack, J. J. Koenderink, and M. A. Viergever. Scale space: its natural operators and differential invariants. In A. C. F. Colchester and D. J. Hawkes, editors, Information Processing in Medical Imaging, page 239, Wye, UK, July 1991. IPMI.

    Google Scholar 

  21. J. Zhengping and P. H. Mowforth. Mapping between MR brain images and a voxel model. Med Inf (Lond), 16(2):183–93, Apr–Jun 1991.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Karl Heinz Höhne Ron Kikinis

Rights and permissions

Reprints and permissions

Copyright information

© 1996 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Collins, D.L., Le Goualher, G., Venugopal, R., Caramanos, A., Evans, A.C., Barillot, C. (1996). Cortical constraints for non-linear cortical registration. In: Höhne, K.H., Kikinis, R. (eds) Visualization in Biomedical Computing. VBC 1996. Lecture Notes in Computer Science, vol 1131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0046968

Download citation

  • DOI: https://doi.org/10.1007/BFb0046968

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61649-8

  • Online ISBN: 978-3-540-70739-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics