Robust Global Registration through Geodesic Paths on an Empirical Manifold with Knee MRI from the Osteoarthritis Initiative (OAI)

  • Claire R. Donoghue
  • Anil Rao
  • Anthony M. J. Bull
  • Daniel Rueckert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7359)

Abstract

Accurate affine registrations are crucial for many applications in medical image analysis. Within the Osteoarthritis Initiative (OAI) dataset we have observed a failure rate of approximately 4% for direct affine registrations of knee MRI without manual initialisation. Despite this, the problem of robust affine registration has not received much attention in recent years. With the increase in large medical image datasets, manual intervention is not a suitable solution to achieve successful affine registrations. We introduce a framework to improve the robustness of affine registrations without prior manual initialisations. We use 10,307 MR images from the large dataset available from the OAI to model the low dimensional manifold of the population of unregistered knee MRIs as a sparse k-nearest-neighbour graph. Affine registrations are computed in advance for nearest neighbours only. When a pairwise image registration is required the shortest path across the graph is extracted to find a geodesic path on the empirical manifold. The precomputed affine transformations on this path are composed to find an estimated transformation. Finally a refinement step is used to further improve registration accuracy. Failure rates of geodesic affine registrations reduce to 0.86% with the registration framework proposed.

Keywords

Iterative Close Point Geodesic Path Manifold Learning Medical Image Analysis Local Registration 
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.

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References

  1. 1.
    Tamez-Pena, J., Gonzalez, P., Farber, J., Baum, K., Schreyer, E., Totterman, S.: Atlas based method for the automated segmentation and quantification of knee features: Data from the osteoarthritis initiative. In: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1484–1487 (2011)Google Scholar
  2. 2.
    Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE Transactions on Medical Imaging 29(1), 55–64 (2010)CrossRefGoogle Scholar
  3. 3.
    Carballido-Gamio, J., Majumdar, S.: Atlas-based knee cartilage assessment. Magnetic Resonance in Medicine 66(2), 575–581 (2011)CrossRefGoogle Scholar
  4. 4.
    Rueckert, D., Sonoda, L., Hayes, C., Hill, D., Leach, M., Hawkes, D.: Nonrigid registration using free-form deformations: application to breast mr images. IEEE Transactions on Medical Imaging 18(8), 712–721 (1999)CrossRefGoogle Scholar
  5. 5.
    Donoghue, C., Rao, A., Bull, A.M.J., Rueckert, D.: Manifold learning for automatically predicting articular cartilage morphology in the knee with data from the osteoarthritis initiative (oai). In: SPIE Medical Imaging 2011: Image Processing, Proc., vol. 7962, p. 12 (2011)Google Scholar
  6. 6.
    Yang, G., Stewart, C.V., Sofka, M., Tsai, C.L.: Registration of challenging image pairs: Initialization, estimation, and decision. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 1973–1989 (2007)CrossRefGoogle Scholar
  7. 7.
    Hill, D.L.G., Batchelor, P.G., Holden, M., Hawkes, D.J.: Medical image registration. Physics in Medicine and Biology 46(3), R1–R45 (2001)Google Scholar
  8. 8.
    Studholme, C.: An overlap invariant entropy measure of 3d medical image alignment. Pattern Recognition 32(1), 71–86 (1999)CrossRefGoogle Scholar
  9. 9.
    Hamm, J., Ye, D.H., Verma, R., Davatzikos, C.: Gram: A framework for geodesic registration on anatomical manifolds. Medical Image Analysis 14(5), 633–642 (2010)CrossRefGoogle Scholar
  10. 10.
    Gerber, S., Tasdizen, T., Thomas Fletcher, P., Joshi, S., Whitaker, R.: Manifold modeling for brain population analysis. Medical Image Analysis 14(5), 643–653 (2010)CrossRefGoogle Scholar
  11. 11.
    Wolz, R., Aljabar, P., Hajnal, J.V., Hammers, A., Rueckert, D.: Leap: learning embeddings for atlas propagation. NeuroImage 49(2), 1316–1325 (2010)CrossRefGoogle Scholar
  12. 12.
    Jia, H., Wu, G., Wang, Q., Shen, D.: Absorb: Atlas building by self-organized registration and bundling. NeuroImage 51(3), 1057–1070 (2010)CrossRefGoogle Scholar
  13. 13.
    Peterfy, C., Schneider, E., Nevitt, M.: The osteoarthritis initiative: report on the design rationale for the magnetic resonance imaging protocol for the knee. Osteoarthritis and Cartilage 16(12), 1433–1441 (2008)CrossRefGoogle Scholar
  14. 14.
    Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische Mathematik 1(1), 269–271 (1959)MathSciNetMATHCrossRefGoogle Scholar
  15. 15.
    Tenenbaum, J.B.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)CrossRefGoogle Scholar
  16. 16.
    Indyk, P., Motwani, R.: Approximate nearest neighbors: Towards removing the curse of dimensionality. In: STOC 1998 Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing, pp. 604–613 (1998)Google Scholar
  17. 17.
    Bentley, J.L.: Multidimensional binary search trees used for associative searching. Communications of the ACM 18(9), 509–517 (1975)MathSciNetMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Claire R. Donoghue
    • 1
  • Anil Rao
    • 1
  • Anthony M. J. Bull
    • 2
  • Daniel Rueckert
    • 1
  1. 1.Department of ComputingImperial College LondonLondonUK
  2. 2.Department of BioengineeringImperial College LondonLondonUK

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