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Statistical Regularization of Deformation Fields for Atlas-Based Segmentation of Bone Scintigraphy Images

  • Karl Sjöstrand
  • Mattias Ohlsson
  • Lars Edenbrandt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5761)

Abstract

The construction and application of statistical models of deformations based on non-rigid image registration methods have gained recent popularity. This paper presents the application of such a model to restricting a general-purpose registration algorithm to anatomically plausible solutions. Specifically, the Morphon registration method is used for atlas-based segmentation of bone scintigraphy images. From a training set of 734 images, a model of characteristic deformation fields is built and used for regularizing the registration of 113 test images. Results show that around 300 training images and 30 principal modes are sufficient for building a useful model. The segmentation succeeded in 106 of 113 test images.

Keywords

Training Image Source Image Statistical Shape Model Deformation Vector Temporal Subtraction 
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.
    Miller, P.D., Eardley, I., Kirby, R.S.: Prostate specific antigen and bone scan correlation in the staging and monitoring of patients with prostatic cancer. British Journal of Urology 70, 295–298 (1992)CrossRefGoogle Scholar
  2. 2.
    Knutsson, H., Andersson, M.: Morphons: Paint on priors and elastic canvas for segmentation and registration. In: Scandinavian Conference on Image Analysis, SCIA (2005)Google Scholar
  3. 3.
    Rueckert, D., Frangi, A.F., Schnabel, J.A.: Automatic construction of 3D statistical deformation models using non-rigid registration. IEEE Transactions on Medical Imaging 22, 77–84 (2003)CrossRefMATHGoogle Scholar
  4. 4.
    Thompson, S., Penney, G., Buie, D., Dasgupta, P., Hawkes, D.: Use of a CT statistical deformation model for multi-modal pelvic bone segmentation. In: SPIE Medical Imaging 2008: Image Processing, vol. 6914, pp. 9141–9141. SPIE (2008)Google Scholar
  5. 5.
    Pettersson, J., Knutsson, H., Borga, M.: Automatic hip bone segmentation using non-rigid registration. In: Proceedings of the IEEE International Conference on Pattern Recognition, Hong Kong, China (2006)Google Scholar
  6. 6.
    Loeckx, D., Maes, F., Suetens, P.: Temporal subtraction of thorax CR images using a statistical deformation model. IEEE Transactions on Medical Imaging 22, 1490–1504 (2003)CrossRefGoogle Scholar
  7. 7.
    Wouters, J., D’Agostino, E., Maes, F., Vandermeulen, D., Suetens, P.: Non-rigid brain image registration using a statistical deformation model, vol. 6144., 614411. SPIE (2006)Google Scholar
  8. 8.
    Frangi, A.F., Rueckert, D., Schnabel, J., Niessen, W.J.: Automatic 3D ASM construction via atlas-based landmarking and volumetric elastic registration. In: Insana, M.F., Leahy, R.M. (eds.) IPMI 2001. LNCS, vol. 2082, pp. 78–91. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  9. 9.
    Heitz, G., Rohlfing, T., Maurer Jr., C.R.: Statistical shape model generation using nonrigid deformation of a template mesh. In: Fitzpatrick, J.M., Reinhardt, J.M. (eds.) Proceedings of the SPIE, Medical Imaging 2005: Image Processing, vol. 5747, pp. 1411–1421. SPIE (2005)Google Scholar
  10. 10.
    Ólafsdóttir, H., Hansen, M.S., Sjöstrand, K., Darvann, T.A., Hermann, N.V., Oubel, E., Ersbøll, B.K., Larsen, R., Frangi, A.F., Larsen, P., Perlyn, C.A., Morriss-Kay, G.M., Kreiborg, S.: Sparse statistical deformation model for the analysis of craniofacial malformations in the crouzon mouse. In: Ersbøll, B.K., Pedersen, K.S. (eds.) SCIA 2007. LNCS, vol. 4522, pp. 112–121. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Bro-Nielsen, M.: Medical Image Registration and Surgery Simulation. PhD thesis, Department of Mathematical Modelling, Technical University of Denmark (1996)Google Scholar
  12. 12.
    Guimond, A., Meunier, J.: Average brain models: A convergence study. Computer Vision and Image Understanding 77(77), 192–210 (2000)CrossRefGoogle Scholar
  13. 13.
    Jolliffe, I.T.: Principal Component Analysis, 2nd edn. Springer, Heidelberg (2002)MATHGoogle Scholar
  14. 14.
    Horn, J.L.: A rationale and a test for the number of factors in factor analysis. Psychometrika 30, 179–185 (1965)CrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Karl Sjöstrand
    • 1
  • Mattias Ohlsson
    • 1
  • Lars Edenbrandt
    • 1
  1. 1.EXINI Diagnostics ABLundSweden

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