Robust Click-Point Linking for Longitudinal Follow-Up Studies

  • Kazunori Okada
  • Xiaolei Huang
  • Xiang Zhou
  • Arun Krishnan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4091)


This paper proposes a novel framework for robust click-point linking: efficient localized registration that allows users to interactively prescribe where the accuracy has to be high. Given a user-specified point in one domain, it estimates a single point-wise correspondence between a data domain pair. In order to link visually dissimilar local regions, we propose a new strategy that robustly establishes such a correspondence using only geometrical relations without comparing the local appearances. The solution is formulated as a maximum likelihood (ML) estimation of a spatial likelihood model without an explicit parameter estimation. The likelihood is modeled by a Gaussian mixture whose component describes geometric context of the click-point relative to pre-computed scale-invariant salient-region features. The local ML estimation was efficiently achieved by using variable-bandwidth mean shift. Two transformation classes of pure translation and scaling/translation are considered in this paper. The feasibility of the proposed approach is evaluated with 16 pairs of whole-body CT data, demonstrating the effectiveness.


Gaussian Mixture Model Salient Region Reference Domain Localize Registration Pure Translation 
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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  1. 1.
    Kadir, T., Brady, M.: Saliency, scale and image description. International Journal of Computer Vision 45, 83–105 (2001)zbMATHCrossRefGoogle Scholar
  2. 2.
    Huang, X., Sun, Y., Metaxas, D., Sauer, F., Xu, C.: Hybrid image registration based on configural matching of scale-invariant salient region features. In: Second IEEE Workshop on Image and Video Registration (2004)Google Scholar
  3. 3.
    Hahn, D., Sun, Y., Hornegger, J., Xu, C., Wolz, G., Kuwert, T.: A practical salient region feature based 3D multimodality registration method for medical images. In: SPIE Med. Imag. (2006)Google Scholar
  4. 4.
    Comaniciu, D.: An algorithm for data-driven bandwidth selection. IEEE Trans. Pat. Anal. Mach. Intell. 25, 281–288 (2003)CrossRefGoogle Scholar
  5. 5.
    Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: IEEE Conf. on Computer Vision and Pattern Recognition, vol. 2, pp. 264–271 (2003)Google Scholar
  6. 6.
    Epshtein, B., Ullman, S.: Identifying semantically equivalent object fragments. In: IEEE Conf. on Computer Vision and Pattern Recognition, vol. 1, pp. 2–9 (2005)Google Scholar
  7. 7.
    Novak, C., Shen, H., Odry, B., Ko, J., Naidich, D.: System for automatic detection of lung nodules exhibiting growth. In: SPIE Med. Imag. (2004)Google Scholar
  8. 8.
    Pennec, X., Ayache, N., Thirion, J.: Landmark-based registration using features identied through differential geometry. In: Handbook of Medical Imaging, pp. 499–513. Academic Press, London (2000)CrossRefGoogle Scholar
  9. 9.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Comm. of the ACM 24, 381–395 (1981)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kazunori Okada
    • 1
  • Xiaolei Huang
    • 2
  • Xiang Zhou
    • 2
  • Arun Krishnan
    • 2
  1. 1.Department of Computer ScienceSan Francisco State University 
  2. 2.Computer-Aided Diagnosis and Therapy SolutionsSiemens Medical Solutions 

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