Shape Based Identification of Proteins in Volume Images

  • Ida-Maria Sintorn
  • Gunilla Borgefors
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3540)


A template based matching method, adopted to the application of identifying individual proteins of a certain kind in volume images, is presented. Grey-level and gradient magnitude information is combined in the watershed algorithm to extract stable borders. These are used in a subsequent hierarchical matching algorithm. The matching algorithm uses a distance transform to search for local best fits between the edges of a template and edges in the underlying image. It is embedded in a resolution pyramid to decrease the risk of getting stuck in false local minima. This method makes it possible to find proteins attached to other proteins, or proteins appearing as split into parts in the images. It also decreases the amount of human interaction needed for identifying individual proteins of the searched kind. The method is demonstrated on a set of three volume images of the antibody IgG in solution.


Volume Image Volume Rendering Gradient Magnitude Local Contrast Object Region 
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.


  1. 1.
    Norel, R., Petrey, D., Wolfson, H.J., Nussinov, R.: Examination of shape complementarity in docking of unbound proteins. Prot. Struct. Func. Gen. 36, 307–317 (1999)CrossRefGoogle Scholar
  2. 2.
    Liang, J., Edelsbrunner, H., Woodward, C.: Anatomy of protein pockets and cavities: Measurement of binding site geometry and implications for ligand design. Prot. Sci. 7, 1884–1897 (1998)CrossRefGoogle Scholar
  3. 3.
    Banyay, M., Gilstring, F., Hauzenberger, E., Öfverstedt, L.G., Eriksson, A.B., Krupp, J.J., Larsson, O.: Three-dimensional imaging of in situ specimens with low-dose electron tomography to analyze protein conformation. Assay Drug Dev. Technol. 2, 561–567 (2004)Google Scholar
  4. 4.
    Sandin, S., Öfverstedt, L.G., Wikström, A.C., Wrange, O., Skoglund, U.: Structure and flexibility of individual immunoglobulin G molecules in solution. Structure 12 (2004)Google Scholar
  5. 5.
    Skoglund, U., Öfverstedt, L.G., Burnett, R., Bricogne, G.: Maximum-entropy three-dimensional reconstruction with deconvolution of the contrast transfer function: A test application with adenovirus. J. Struct. Biol. 117, 173–188 (1996)CrossRefGoogle Scholar
  6. 6.
    Beucher, S., Lantuéjoul, C.: Use of watersheds in contour detection. In: International Workshop on Image Processing: Real-time and Motion Detection/ Estimation, 2.1–2.12 (1979)Google Scholar
  7. 7.
    Meyer, F., Beucher, S.: Morphological segmentation. J. Vis. Comm. Im. Repr. 1, 21–46 (1990)CrossRefGoogle Scholar
  8. 8.
    Borgefors, G.: Hierarchical chamfer matching: A parametric edge matching algorithm. IEEE Trans. Pat. Anal. Mach. Intell. 10, 849–865 (1988)CrossRefGoogle Scholar
  9. 9.
    Barrow, H.G., Tenenbaum, J.M., Bolles, R., Wolf, H.C.: Parametric correspondence and chamfer matching: Two new techniques for image matching. In: Proc. 5th Int. Joint Conf. Artif. Intell., Cambridge, Massachusetts, pp. 659–663 (1977)Google Scholar
  10. 10.
    Wählby, C., Sintorn, I.M., Erlandsson, F., Borgefors, G., Bengtsson, E.: Combining intensity, edge, and shape information for 2D and 3D segmentation of cell nuclei in tissue sections. J. Micr. 215, 67–76 (2004)CrossRefGoogle Scholar
  11. 11.
    Vincent, L.: Morphological grayscale reconstruction in image analysis: Applications and efficient algorithms. IEEE Trans. Im. Proc. 2, 176–201 (1993)CrossRefGoogle Scholar
  12. 12.
    Beucher, S.: The watershed transformation applied to image segmentation. Scanning Microscopy 6, 299–314 (1992)Google Scholar
  13. 13.
    Berman, H., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T., Weissig, H., Shindyalova, I., Bourne, P.: The protein data bank. Nucl. Ac. Res. 28, 235–242 (2000)CrossRefGoogle Scholar
  14. 14.
    Pittet, J.J., Henn, C., Engel, A., Heymann, J.B.: Visualizing 3D data obtained from microscopy on the internet. J. Struct. Biol. 125, 123–132 (1999)CrossRefGoogle Scholar
  15. 15.
    Sonka, M., Hlavac, V., Boyle, R.: 4. In: Image Processing, Analysis, and Machine Vision, 2nd edn., pp. 77–88. Brooks/Cole Publishing Company (1999)Google Scholar
  16. 16.
    Vincent, L., Soille, P.: Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Trans. Pat. Anal. Mach. Intell. 13, 583–597 (1991)CrossRefGoogle Scholar
  17. 17.
    Rosenfeld, A., Pfaltz, J.L.: Sequential operations in digital picture processing. J. Ass. Comp. Mach. 13, 471–494 (1966)zbMATHGoogle Scholar
  18. 18.
    Borgefors, G.: On digital distance transforms in three dimensions. Comp. Vis. Im. Underst. 64, 368–376 (1996)CrossRefGoogle Scholar
  19. 19.
    Borgefors, G.: An improved version of the chamfer matching algorithm. In: 7th Int. Conf. on Pat. Rec., Montreal, Canada, pp. 1984–1175 (1984)Google Scholar
  20. 20.
    Nash, S.G., Sofer, A.: 10.5, 11.1. In: Linear and Nonlinear Programming. McGraw- Hill, New York (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Ida-Maria Sintorn
    • 1
  • Gunilla Borgefors
    • 1
  1. 1.Centre for Image AnalysisSwedish University of Agricultural SciencesUppsalaSweden

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