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A Unified Feature Registration Method for Brain Mapping

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Information Processing in Medical Imaging (IPMI 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2082))

Abstract

This paper describes the design, implementation and preliminary results of a unified non-rigid feature registration method for the purpose of brain anatomical structure alignment. We combine different types of features together and fuse them into a common point representation. This enables the co-registration of all features using a new non-rigid point matching algorithm. In this way, the spatial interrelationships between different features are directly utilized to improve the registration accuracy. We also conducted a carefully designed synthetic study to compare some anatomical features’ ability for non-rigid brain structure alignment. This study allows us to evaluate the relative improvements in registration accuracy when different features are combined.

Acknowledgments

The research work is partially supported by an NSF grant IIS-9906081 to A.R. and by an NIH grant R01 NS35193 to J.D. The authors would also like to thank Xenios Papademetris and Oskar Skrinjar for their help in the visualization.

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References

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

    Article  Google Scholar 

  2. F. L. Bookstein. Principal warps: Thin-plate splines and the decomposition of deformations. IEEE Trans. Patt. Anal. Mach. Intell., 11(6):567–585, June 1989.

    Article  MATH  Google Scholar 

  3. G. Christensen. Consistent linear-elastic transformations for image matching. In Proceedings of Information Processing in Medical Imaging-IPMI 99, pages 224–237. Springer-Verlag, 1999.

    Article  Google Scholar 

  4. G. Christensen, S. Joshi, and M. Miller. Volumetric transformation of brain anatomy. IEEE Trans. Med. Imag., 16(6):864–877, 1997.

    Article  Google Scholar 

  5. H. Chui, J. Rambo, J. Duncan, R. Schultz, and A. Rangarajan. Registration of cortical anatomical structures via robust 3D point matching. In Proceedings of Information Processing in Medical Imaging-IPMI 99, pages 168–181. Springer-Verlag, 1999.

    Article  Google Scholar 

  6. H. Chui and A. Rangarajan. A new algorithm for non-rigid point matching. In Proceedings of IEEE Conf. on Computer Vision and Pattern Recognition-CVPR 2000, volume 2, pages 44–51. IEEE Press, 2000.

    Google Scholar 

  7. D. Collins, G. Goualher, and A. Evans. Non-linear cerebral registration with sulcal constraints. InW. Wells, A. Colchester, and S. Delp, editors, Proceedings of Medical Image Computing and Computer-Assisted Intervention-MICCAI 98, pages 974–984. Springer, 1998.

    Google Scholar 

  8. D. Collins, C. Holmes, T. Peters, and A. Evans. Automatic 3D model-based neuroanatomical segmentation. Human Brain Mapping, 3(3):190–208, 1995.

    Article  Google Scholar 

  9. B. Kim C.R. Meyer, J. L. Boes and P. H. Bland. Demonstration of accuracy and clinical verstility of mutual information for automatic multimodality image fusion using affine and thin plate spline warped geometric deformations. Medical Image Analysis, 1(3):195–206, 1997.

    Article  Google Scholar 

  10. C. Davatzikos. Spatial transformation and registration of brain images using elastically deformable models. Computer Vision and Image Understanding: Special Issue on Medical Imaging, 6(2):207–222, 1997.

    Article  Google Scholar 

  11. C. Davatzikos and J.L. Prince. Brain image registration based on curve mapping. Proc. of the IEEE Workshop on Biom. Image Anal., pages 245–254, 1994.

    Google Scholar 

  12. J. Gee. Probabilistic Matching of deformed images. PhD thesis, Dept. of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, 1995.

    Google Scholar 

  13. S. Gold, A. Rangarajan, C. P. Lu, S. Pappu, and E. Mjolsness. New algorithms for 2-D and 3-D point matching: pose estimation and correspondence. Pattern Recognition, 31(8):1019–1031, 1998.

    Article  Google Scholar 

  14. S. Joshi and M. I. Miller. Landmark matching via large deformation diffeomorphisms IEEE Trans. Image Processing, 9(8):1357–1370, 2000.

    Article  MATH  MathSciNet  Google Scholar 

  15. N. Khaneja, M. I. Miller, and U. Grenander. Dynamic programming generation of curves on brain surfaces. IEEE Transaction on Pattern Analysis and Machine Intelligence, 20(1):1260–1265, 1998.

    Article  Google Scholar 

  16. D. MacDonald, N. Kabani, D. Avis, and A. Evans. Automated 3d extraction of inner and outer surfaces of cerebral cortex from MRI. NeuroImage, 12:340–356, 2000.

    Article  Google Scholar 

  17. A. Rangarajan, H. Chui, E. Mjolsness, S. Pappu, L. Davachi, P. Goldman-Rakic, and J. Duncan. Arob ust point matching algorithm for autoradiograph alignment. Medical Image Analysis, 4(1):379–398, 1997.

    Article  Google Scholar 

  18. S. Sandor and R. Leahy. Surface based labeling of cortical anatomy using a deformable atlas. IEEE Trans. Med. Imag., 16(1):41–54, 1997.

    Article  Google Scholar 

  19. P. Thompson, D. MacDonald, M.S. Mega, C.J. Holmes, C.J. Evans, and A.W. Toga. Detection and mapping of abnormal brain structure with a probabilistic atlas of cortical surfaces. Journal of Computer Assisted Tomography, 21(4):567–581, 1997.

    Article  Google Scholar 

  20. P. Thompson and A. W. Toga. A surface-based technique for warping threedimensional images of the brain. IEEE Trans. Med. Imag., 5(4):402–417, August 1996.

    Article  Google Scholar 

  21. A. Toga and J. Mazziotta. Brain Mapping: The Methods. Academic Press, 1996.

    Google Scholar 

  22. M. Vaillant and C. Davatzikos. Hierarchical matching of cortical features for deformable brain image registration. In Proceedings of Information Processing in Medical Imaging-IPMI 99, volume 1613, pages 182–195. Springer-Verlag, 1999.

    Article  Google Scholar 

  23. M. Vaillant, C. Davatzikos, and R. Bryan. Finding 3D parametric representations of the deep cortical folds. In A. Amini, F. L. Bookstein, and D. Wilson, editors, Proc. of the Workshop on Mathematical Methods in Biomedical Image Analysis, pages 151–159. IEEE Computer Society Press, 1996.

    Google Scholar 

  24. G. Wahba. Spline models for observational data. SIAM, Philadelphia, PA, 1990.

    Google Scholar 

  25. Y. Wang and L. H. Staib. Boundary finding with prior shape and smoothness models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(7):738–743, July 2000.

    Article  Google Scholar 

  26. C. Xu, D. Pham, and J. L. Prince. Reconstruction of the central layer of the human cerebral cortex from MR images. In Proceedings of Medical Image Computing and Computer-Assisteed Intervention-MICCAI 98, pages 481–488, 1998.

    Google Scholar 

  27. X. Zeng, L. H. Staib, H. Tagare R. T. Schultz, and J. S. Duncan. An ew approach to 3d sulcal ribbon finding from MR images. In Proceedings of Medical Image Computing and Computer Assisted Intervention-MICCAI 99, pages 148–157, 1999.

    Article  Google Scholar 

  28. X. Zeng, L. H. Staib, R. T. Schultz, and J. S. Duncan. Segmentation and measurement of the cortex from 3D MR images using coupled-surfaces propagation. IEEE Transaction on Medical Imaging, 18(10):927–937, 1999.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Yale University, New Haven, CT, USA

    Haili Chui & James Duncan

  2. Department of Diagnostic Radiology, New Haven, CT, USA

    James Duncan

  3. Yale Child Study Center, Yale University, New Haven, CT, USA

    Lawrence Win & Robert Schultz

  4. Department of Computer, Information Science and Engineering, University of Florida, Gainesville, FL, USA

    Anand Rangarajan

Authors
  1. Haili Chui
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  2. Lawrence Win
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  3. Robert Schultz
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  4. James Duncan
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  5. Anand Rangarajan
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Editor information

Editors and Affiliations

  1. Biomedical Engineering, University of California, One Shields Avenue, Davis, CA, 95616, USA

    Michael F. Insana

  2. Signal and Image Processing Institute, University of Southern California, 3740 McClintock Avenue, Los Angeles, CA, 90089-2564, USA

    Richard M. Leahy

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© 2001 Springer-Verlag Berlin Heidelberg

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Chui, H., Win, L., Schultz, R., Duncan, J., Rangarajan, A. (2001). A Unified Feature Registration Method for Brain Mapping. In: Insana, M.F., Leahy, R.M. (eds) Information Processing in Medical Imaging. IPMI 2001. Lecture Notes in Computer Science, vol 2082. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45729-1_31

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  • DOI: https://doi.org/10.1007/3-540-45729-1_31

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42245-7

  • Online ISBN: 978-3-540-45729-9

  • eBook Packages: Springer Book Archive

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