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German Conference on Pattern Recognition

DAGM 2015: Pattern Recognition pp 390–399Cite as

Fast Brain MRI Registration with Automatic Landmark Detection Using a Single Template Image

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9358))

Abstract

Automatic registration of brain MR images is still a challenging problem. We have chosen an approach based on landmarks matching. However, manual landmarking of the images is cumbersome. Existing algorithms for automatic identification of pre-defined set of landmarks usually require manually landmarked training bases. We propose the registration algorithm that involves automatic detection of landmarks with the use of only one manually landmarked template image. Landmarks are detected using Canny edge detector and point descriptors. Evaluation of four types of descriptors showed that SURF provides the best trade-off between speed and accuracy. Thin plate spline transformation is used for landmark-based registration. The proposed algorithm was compared with the best existing registration algorithm without the use of local features. Our algorithm showed significant speed-up and better accuracy in matching of anatomical structures surrounded by the landmarks. All the experiments were performed on the IBSR database.

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References

  1. Insight segmentation and registration toolkit (ITK). http://www.itk.org

  2. Internet brain segmentation repository. http://www.nitrc.org/projects/ibsr

  3. Andersson, J., Smith, S., Jenkinson, M.: FNIRT – FMRIB’s non-linear image registration tool. Human Brain Mapping. Poster 496 (2008)

    Google Scholar 

  4. Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12(1), 26–41 (2008)

    Article  Google Scholar 

  5. Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)

    Article  Google Scholar 

  6. Cabezas, M., Oliver, A., Llad, X., et al.: A review of atlas-based segmentation for magnetic resonance brain images. Comput. Methods Programs Biomed. 104(3), e158–e177 (2011)

    Article  Google Scholar 

  7. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)

    Article  Google Scholar 

  8. Collins, D.L., Evans, A.C.: ANIMAL: validation and applications of nonlinear registration-based segmentation. Int. J. Pattern Recognit Artif Intell. 11(8), 1271–1294 (1997)

    Article  Google Scholar 

  9. Davis, M.H., Khotanzad, A., Flamig, D.P., Harms, S.E.: A physics-based coordinate transformation for 3-D image matching. IEEE Trans. Med. Imaging 16(3), 317–328 (1997)

    Article  Google Scholar 

  10. Diez, Y., Gubern-Mérida, A., Wang, L., Diekmann, S., Martí, J., Platel, B., Kramme, J., Martí, R.: Comparison of methods for current-to-prior registration of breast DCE-MRI. In: Fujita, H., Hara, T., Muramatsu, C. (eds.) IWDM 2014. LNCS, vol. 8539, pp. 689–695. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  11. Evans, A.C., Dai, W., Collins, L., et al.: Warping of a computerized 3-D atlas to match brain image volumes for quantitative neuroanatomical and functional analysis. In: Proceedings SPIE, pp. 236–246. SPIE Press, Bellingham (1991)

    Google Scholar 

  12. Guerrero, R., Pizarro, L., Wolz, R., Rueckert, D.: Landmark localization in brain mr images using feature point descriptors based on 3D local self-similarities. In: 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1535–1538. IEEE Press, New York (2012)

    Google Scholar 

  13. Han, D., Gao, Y., Wu, G., Yap, P.-T., Shen, D.: Robust anatomical landmark detection for MR brain image registration. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part I. LNCS, vol. 8673, pp. 186–193. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  14. Klein, A., Andersson, J., Ardekani, A.B., et al.: Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage 46(3), 786–802 (2009)

    Article  Google Scholar 

  15. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)

    Article  Google Scholar 

  16. Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)

    Article  Google Scholar 

  17. Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A.: Mutual-information-based registration of medical images: a survey. IEEE Trans. Med. Imaging 22(8), 986–1004 (2003)

    Article  Google Scholar 

  18. Rohr, K., Stiehl, H.S., Sprengel, R., et al.: Landmark-based elastic registration using approximating thin-plate splines. IEEE Trans. Med. Imaging 20(6), 526–534 (2001)

    Article  Google Scholar 

  19. Shechtman, E., Irani, M.: Matching local self-similarities across images and videos. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8. IEEE Press, New York (2007)

    Google Scholar 

  20. Shen, D.: Fast image registration by hierarchical soft correspondence detection. Pattern Recogn. 42(5), 954–961 (2009)

    Article  Google Scholar 

  21. Smith, S.M., Jenkinson, M., Woolrich, M.W., et al.: Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23(1), 208–219 (2004)

    Article  Google Scholar 

  22. Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Non-parametric diffeomorphic image registration with the demons algorithm. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 319–326. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  23. Wu, G., Kim, M., Wang, Q., Shen, D.: S-HAMMER: hierarchical attribute-guided, symmetric diffeomorphic registration for MR brain images. Hum. Brain Mapp. 35(3), 1044–1060 (2014)

    Article  Google Scholar 

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Acknowledgments

The authors would like to thank Alexey V. Petraikin, M.D. from Pirogov Russian National Research Medical University (RNRMU) for valuable discussion.

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Authors and Affiliations

  1. Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, GSP-1, Leninskie Gory, Moscow, 119991, Russian Federation

    Olga V. Senyukova & Denis S. Zobnin

Authors
  1. Olga V. Senyukova
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  2. Denis S. Zobnin
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Corresponding author

Correspondence to Olga V. Senyukova .

Editor information

Editors and Affiliations

  1. Institute of Computer Science III, University of Bonn, Bonn, Germany

    Juergen Gall

  2. MPI for Intelligent Systems, University of Tübingen, Tübingen, Germany

    Peter Gehler

  3. Computer Vision Group, Visual Computing Institute, RWTH Aachen, Aachen, Germany

    Bastian Leibe

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© 2015 Springer International Publishing Switzerland

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Senyukova, O.V., Zobnin, D.S. (2015). Fast Brain MRI Registration with Automatic Landmark Detection Using a Single Template Image. In: Gall, J., Gehler, P., Leibe, B. (eds) Pattern Recognition. DAGM 2015. Lecture Notes in Computer Science(), vol 9358. Springer, Cham. https://doi.org/10.1007/978-3-319-24947-6_32

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  • DOI: https://doi.org/10.1007/978-3-319-24947-6_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24946-9

  • Online ISBN: 978-3-319-24947-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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