Towards Unsupervised Segmentation of Semi-rigid Low-Resolution Molecular Surfaces

  • Yusu Wang
  • Leonidas J. Guibas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4077)


In this paper, we study a particular type of surface segmentation problem motivated by molecular biology applications. In particular, two input surfaces are given, coarsely modeling two different conformations of a molecule undergoing a semi-rigid deformation. The molecule consists of two subunits that move in a roughly rigid manner. The goal is to segment the input surfaces into these semi-rigid subcomponents. The problem is closely related to non-rigid surface registration problems, although considering only a special type of deformation that exists commonly in macromolecular movements (such as the popular hinge motion). We present and implement an efficient paradigm for this problem, which combines several existing and new ideas. We demonstrate the performance of our new algorithm by some preliminary experimental results in segmenting low-resolution molecular surfaces.


Geodesic Distance Iterative Close Point Normal Mode Analysis Rigid Transformation Iterative Close Point 
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.
    Girod, B., Greiner, G., Niemann, H. (eds.): Principles of 3D image analysis and synthesis. Kluwer Academic Publishers, Dordrecht (2000)MATHGoogle Scholar
  2. 2.
    Yoo, T. (ed.): Insight into images: Principles and practices for segmentation, registration, and image analysis. A. K. Peters (2004)Google Scholar
  3. 3.
    Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis an Machine Intelligence 24(4), 509–522 (2002)CrossRefGoogle Scholar
  4. 4.
    Barequet, G., Sharir, M.: Partial surface and volume matching in three dimensions. IEEE Trans. Pattern Anal. Mach. Intell. 19(9), 929–948 (1997)CrossRefGoogle Scholar
  5. 5.
    Gelfand, N., Mitra, N.J., Guibas, L.J., Pottmann, H.: Robust global registration. In: Proc. Symp. Geom. Processing, pp. 197–206 (2005)Google Scholar
  6. 6.
    Johnson, A.E., Hebert, M.: Using spin images for efficient object recognition in cluttered 3D scences. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(5), 433–449 (1999)CrossRefGoogle Scholar
  7. 7.
    Koenderink, J.J.: Solid shape. MIT Press, Cambridge (1990)Google Scholar
  8. 8.
    Manay, S., Hong, B., Yezzi, A.J., Soatto, S.: Integral invariant signatures. In: European Conference on Computer Vision, pp. 87–99 (2004)Google Scholar
  9. 9.
    Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(2), 239–256 (1992)CrossRefGoogle Scholar
  10. 10.
    Wolfson, H.J., Rigoutsos, I.: Geometric hashing: An overview. IEEE Computational Science and Engineering 4(4), 10–21 (1997)CrossRefGoogle Scholar
  11. 11.
    Metaxas, D.N.: Physics-Based Deformable Models. Kluwer Academic, Dordrecht (1997)Google Scholar
  12. 12.
    Ruechert, D., Hawkes, D.J.: Registration of biomedical images. In: Baldock, R., Graham, J. (eds.) Image Processing and Analysis - A Practical Approach. Oxford University Press, Oxford (1999)Google Scholar
  13. 13.
    Rueckert, D.: Non-rigid registration: Techniques and applications. In: Hajnal, J.V., Hill, D.L.G., Hawkes, D.J. (eds.) Medical Image Registration. CRC Press, Boca Raton (2001)Google Scholar
  14. 14.
    Sclaroff, S., Pentland, A.P.: Modal matching for correspondence and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 17(6), 545–561 (1995)CrossRefGoogle Scholar
  15. 15.
    Noh, J.-Y., Neumann, U.: Expression cloning. In: SIGGRAPH 2001: Proceedings of the 28th annual conference on Computer graphics and interactive techniques, pp. 277–288 (2001)Google Scholar
  16. 16.
    Pauly, M., Mitra, N.J., Giesen, J., Gross, M., Guibas, L.: Example-based 3d scan completion. In: Symposium on Geometry Processing, pp. 23–32 (2005)Google Scholar
  17. 17.
    Allen, B., Curless, B., Popovió, Z.: The space of human body shapes. ACM Transactions on Graphics 22(3), 587–594 (2003)CrossRefGoogle Scholar
  18. 18.
    Guenter, B., Grimm, C., Wood, D., Wmlvar, H., Pighin, F.: Making faces. In: SIGGRAPH 1998: Proceedings of the 28th annual conference on Computer graphics and interactive techniques, pp. 55–66 (1998)Google Scholar
  19. 19.
    Kalberer, G.A., Gool, L.V.: Face animation based on observed 3d speech dynamics. In: IEEE Conference on Computer Animation, pp. 20–27 (2001)Google Scholar
  20. 20.
    Kakadiaris, I.A., Metaxas, D., Bajcsy, R.: Active part-decomposition, shape and motion estimation of articulated objects: a physics-based approach. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 980–984 (1994)Google Scholar
  21. 21.
    Zelnik-Manor, L., Machline, M., Irani, M.: Multi-body factorization with uncertainty: Revisiting motion consistency. IJCV special issue on Vision and Modeling of Dynamic Scenes (to appear, 2006)Google Scholar
  22. 22.
    Holm, L., Sander, C.: Protein structure comparison by alignment of distance matrices. J. Mol. Biol. 233, 123–138 (1993)CrossRefGoogle Scholar
  23. 23.
    Shindyalov, I.N., Bourne, P.E.: Protein structure alignment by incremental combinatorial extension (CE) of optimal path. Protein Engineering 11(9), 739–747 (1998)CrossRefGoogle Scholar
  24. 24.
    Pearl, F.M.G., Lee, D., Bray, J.E., Sillitoe, I., Todd, A.E., Harrison, A.P., Thornton, J.M., Orengo, C.A.: Assigning genomic sequences to CATH. Nucleic Acids Research 28(1), 277–282 (2000)CrossRefGoogle Scholar
  25. 25.
    Chen, R., Li, L., Weng, Z.: ZDOCK: An initial-stage protein docking algorithm. Proteins 52(1), 80–87 (2003)CrossRefGoogle Scholar
  26. 26.
    Smith, G.R., Sternberg, M.J.E.: Prediction of protein-protein interactions by docking methods. Current Opinion in Structural Biology 12, 29–35 (2002)CrossRefGoogle Scholar
  27. 27.
    Wang, Y., Agarwal, P.K., Brown, P., Edelsbrunner, H., Rudolph, J.: Coarse and reliable geometric alignment for protein docking. In: Pac. Symp. Biocomput., pp. 66–77 (2005)Google Scholar
  28. 28.
    Thomas, A., Hinsen, K., Field, M.J., Perahia, D.: Tertiary and quaternary conformational changes in aspartate transcarbamylase: a normal mode study. Proteins 34, 96–112 (1999)CrossRefGoogle Scholar
  29. 29.
    Alexandrov, V., Lehnert, U., Echols, N., Milburn, D., Engelman, D., Gerstein, M.: Normal modes for predicting protein motions: a comprehensive database assesement and associated web tool. Protein Science 14(3), 633–643 (2005)CrossRefGoogle Scholar
  30. 30.
    Tama, F., Miyashita, O., Brooks III, C.L.: Normal mode based flexible fitting of high-resolution structure into low-resolution experimental data from cryo-em. Journal of Structural Biology 147, 315–326 (2004)CrossRefGoogle Scholar
  31. 31.
    Tama, F., Sanejouand, Y.H.: Conformational change of proteins arising from normal mode calculations. Protein Engineering 14, 1–6 (2001)CrossRefGoogle Scholar
  32. 32.
    Delarue, M., Dumas, P.: On the use of low-frequency normal modes to enforce collective movements in refining macromolecular structural models. Proceedings of National Academy of Science 101(18), 6957–6962 (2004)CrossRefGoogle Scholar
  33. 33.
    Ming, D., Kong, Y., Wakil, S.J., Brink, J., Ma, J.: Domain movements in human fatty acid synthase by quantized elastic deformational model. Proceedins of National Academy of Science 99(12), 7895–7899 (2002)CrossRefGoogle Scholar
  34. 34.
    Tama, F., Wriggers, W., Brooks III, C.L.: Exploring global distortions of biological macromolecules and assemblies from low-resolution structural information and elastic network theory. Journal of Molecular Biology 321, 297–305 (2002)CrossRefGoogle Scholar
  35. 35.
    Wriggers, W., Schulten, K.: Protein domain movements: detetion of rigid domains and visualization of hinges in comparisons of atomic coordinates. Proteins 29, 1–14 (1997)CrossRefGoogle Scholar
  36. 36.
    Krebs, W.G., Gerstein, M.: The morph server: a standardized system for analyzing and visualizing macromolecular motions in a database framework. Nucleic Acids Research 28(8), 1665–1675 (2000)CrossRefGoogle Scholar
  37. 37.
    Agarwal, P.K., Edelsbrunner, H., Harer, J., Wang, Y.: Extreme elevation on a 2-manifold. In: Proc. 20th Sympos. Comput. Geom., pp. 357–365 (2004)Google Scholar
  38. 38.
    Ceulemans, H., Russell, R.B.: Fast fitting of atomic structures to low-resolution electron density maps by surface overlap maximization. Journal of Molecular Biology 338, 783–793 (2004)CrossRefGoogle Scholar
  39. 39.
    Ludtke, S., Jiang, W., Peng, L., Tang, G., Baldwin, P., Fang, S., Khant, H., Nason, L.: EMAN software package (2006),
  40. 40.
    Bauer, C.B., Holden, H.W., Thoden, J.B., Smith, R., Rayment, I.: X-ray structures of the apo and MgATP-bound states of dictyostelium discoideum myosin motor domain. J. Biol. Chem. 275, 38494–38499 (2000)CrossRefGoogle Scholar
  41. 41.
    Smith, C.A., Rayment, I.: X-ray structure of the magnesium(II).ADP.vanadate complex of the dictyostelium discoideum myosin motor domain to 1.9Åresolution. Biochemistry 35, 5405–5417 (1996)Google Scholar
  42. 42.
    Alexa, M., Cohen-Or, D., Levin, D.: As-rigid-as-possible shape interpolation. In: SIGGRAPH 2000: Proceedings of the 27th annual conference on Computer graphics and interactive techniques, pp. 157–164. ACM Press/Addison-Wesley Publishing Co., New York (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yusu Wang
    • 1
  • Leonidas J. Guibas
    • 2
  1. 1.Department of Computer Science and Engineeringthe Ohio State UniversityColumbusUSA
  2. 2.Department of Computer ScienceStanford UniversityStanfordUSA

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