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Fast Shape-Based Nearest-Neighbor Search for Brain MRIs Using Hierarchical Feature Matching

  • Peihong Zhu
  • Suyash P. Awate
  • Samuel Gerber
  • Ross Whitaker
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6892)

Abstract

This paper presents a fast method for quantifying shape differences/similarities between pairs of magnetic resonance (MR) brain images. Most shape comparisons in the literature require some kind of deformable registration or identification of exact correspondences. The proposed approach relies on an optimal matching of a large collection of features, using a very fast, hierarchical method from the literature, called spatial pyramid matching (SPM). This paper shows that edge-based image features in combination with SPM results in a fast similarity measure that captures relevant anatomical information in brain MRI. We present extensive comparisons against known methods for shape-based, k-nearest-neighbor lookup to evaluate the performance of the proposed method. Finally, we show that the method compares favorably with more computation-intensive methods in the construction of local atlases for use in brain MR image segmentation.

Keywords

Registration Method Deformable Registration Spatial Pyramid Match Hierarchical Feature Tissue Probability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Aljabar, P., Heckemann, R., Hammers, A., Hajnal, J., Rueckert, D.: Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy. NeuroImage 46(3), 726–738 (2009)CrossRefGoogle Scholar
  2. 2.
    Aljabar, P., Wolz, R., Srinivasan, L., Counsell, S., Boardman, J., Murgasova, M., Doria, V., Rutherford, M., Edwards, A., Hajnal, J., Rueckert, D.: Combining morphological information in a manifold learning framework: Application to neonatal MRI. In: Jiang, T., Navab, N., Pluim, J.P., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6363, pp. 1–8. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  3. 3.
    Aubert-Broche, B., Collins, D., Evans, A.: A new improved version of the realistic digital brain phantom. Neuro Image 32(1), 138–145 (2006)Google Scholar
  4. 4.
    Beg, F., Miller, M., Trouve, A., Younes, L.: Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int. J. Comp. Vis. 61(2) (2005)Google Scholar
  5. 5.
    Chen, T., Rangarajan, A., Eisenschenk, S.J., Vemuri, B.C.: Construction of neuroanatomical shape complex atlas from 3D brain MRI. In: Jiang, T., Navab, N., Pluim, J.P., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6363, pp. 65–72. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Depa, M., Sabuncu, M.R., Holmvang, G., Nezafat, R., Schmidt, E.J., Golland, P.: Robust atlas-based segmentation of highly variable anatomy: Left atrium segmentation. In: Camara, O. (ed.) MICCAI Workshop Stat. Atlases Comp. Models Heart, pp. 1–8 (2010)Google Scholar
  7. 7.
    Gerber, S., Tasdizen, T., Fletcher, P., Joshi, S., Whitaker, R.: ADNI: Manifold modeling for brain population analysis. Med. Imag. Analysis 14(5), 643–653 (2010)CrossRefGoogle Scholar
  8. 8.
    Grauman, K., Darrell, T.: The pyramid match kernel: Efficient learning with sets of image features. J. Mach. Learn. Res. 2, 725–760 (2007)zbMATHGoogle Scholar
  9. 9.
    Ha, L., Kruger, J., Fletcher, T., Joshi, S., Silva, C.T.: Fast parallel unbiased diffeomorphic atlas construction on multi-graphics processing units. In: Euro. Symp. Parallel Graph. Vis., pp. 65–72 (2009)Google Scholar
  10. 10.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 2169–2178 (2006)Google Scholar
  11. 11.
    Lotjonen, J., Wolz, R., Koikkalainen, J., Thurfjell, L., Waldemar, G., Soininen, H., Rueckert, D.: ADNI: Fast and robust multi-atlas segmentation of brain magnetic resonance images. NeuroImage 49(3), 2352–2365 (2010)CrossRefGoogle Scholar
  12. 12.
    Shen, D., Davatzikos, C.: HAMMER: Hierarchical attribute matching mechanism for elastic registration. IEEE Trans. Med. Imag. 21(11), 1421–1439 (2002)CrossRefGoogle Scholar
  13. 13.
    Wolz, R., Aljabar, P., Hajnal, J.V., Rueckert, D.: Manifold learning for biomarker discovery in MR imaging. In: Wang, F., Yan, P., Suzuki, K., Shen, D. (eds.) Conf. Mach. Learn. Med. Imag., pp. 116–123 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Peihong Zhu
    • 1
  • Suyash P. Awate
    • 1
  • Samuel Gerber
    • 1
  • Ross Whitaker
    • 1
  1. 1.Scientific Computing and Imaging InstituteUniversity of UtahUSA

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