›› 2010, Vol. 25 ›› Issue (3): 614-622.

• Computer Graphics and Visualization • Previous Articles     Next Articles

Geometric Bone Modeling: From Macro to Micro Structures

Oded Zaideman and Anath Fischer   

  1. Laboratory for Computer Aided Design &|Life Cycle Engineering, Faculty of Mechanical Engineering, Technion Israel Institute of Technology, Haifa, Israel 32000
  • Received:2010-01-12 Revised:2010-01-13 Online:2010-05-05 Published:2010-05-05
  • About author:
    Oded Zaideman obtained his B.Sc. degree in 2002 and his M.Sc. degree in 2009 in the Faculty of Mechanical Engineering at the Technion, Haifa, Israel. His current research interests are geometric reconstruction methods and grid-based modeling of bone microstructure.
    Anath Fischer is a professor in the Faculty of Mechanical Engineering at the Technion Israel Institute of Technology (IIT). Her major research interests focus on computational geometry methods for advanced scanning technologies. She has published over 130 papers in academic journals, including IEEE Trans. Visualization & Computer Graphics, CAD J., ASME Trans. JCISE, IJNME, and at international conferences, including Pacific Graphics, SMI and SPM. She is on the editorial boards of ASME Trans. JCISE and served as a guest editor for the Int. Journal of Shape Modeling, ASME Trans. JCISE and J. Computers and Graphic. She has been program chair, conference co-chair and IPC member in international conferences including the program chair of SPM'2010. She was the program chair of ASME ESDA'2008 with 400 participants from 35 countries.

There is major interest within the bio-engineering community in developing accurate and non-invasive means for visualizing, modeling and analyzing bone micro-structures. Bones are composed of hierarchical bio-composite materials characterized by complex multi-scale structural geometry. The process of reconstructing a volumetric bone model is usually based upon CT/MRI scanned images. Meshes generated by current commercial CAD systems cannot be used for further modeling or analysis. Moreover, recently developed methods are only capable of capturing the micro-structure for small volumes (biopsy samples). This paper examines the problem of re-meshing a 3D computerized model of bone micro-structure. The proposed method is based on the following phases: defining sub-meshes of the original model in a grid-based structure, remeshing each sub-mesh using the neural network (NN) method, and merging the sub-meshes into a global mesh. Applying the NN method to micro-structures proved to be quite time consuming. Therefore, a parallel, grid-based approach was applied, yielding a simpler structure in each grid cell. The performance of this method is analyzed, and the method is demonstrated on real bone micro-structures. Furthermore, the method may be used as the basis for generating a multi-resolution bone geometric model.


[1] Weiner S, Wagner H D. The material bone: Structuremechanical function relations. Annual Review of Materials Science, Aug. 1998, 28: 271-298.

[2] Holdstein Y, Fischer A. 3D surface reconstruction using meshing growing neural gas. The Visual Computer, 2008, Special Edition, 24(4): 295-302.

[3] Amenta N, Bern M. Surface Reconstruction by Voronoi Filtering. ACM Press. 1998, pp.39-48.

[4] Boissonnat J D. Representing 2D and 3D shapes with the Delaunay triangulation. In Proc. ICPR84, Montreal, Canada, 1984, pp.745-748.

[5] Hoppe H, DeRose T, Duchamp T, McDonald J, Stuetzle W. Surface Reconstruction from Unorganized Points. ACM Press, 1992, pp.71-78.

[6] Lorensen W E, Cline H E. Marching cubes: A high resolution 3D surface construction algorithm. ACM SIGGRAPH Computer Graphics, 1987, 21(4): 163-169.

[7] Azernikov S, Fischer A. Efficient surface reconstruction method for distributed CAD. Computer Aided Design J., 2004, 36(9): 799-808.

[8] Barhak J, Fischer A. Adaptive reconstruction of freeform objects with 3D SOM neural network grids. Computer & Graphics, 2002, 26(5): 745-751.

[9] Fritzke B. A Growing Neural Gas Network Learns Topologies. Advances in Neural Information Processing Systems 7. Cambridge: MIT Press, MA, 1995.

[10] Martinetz T, Schulten K. A Neural-Gas Network Learns Topologies. Artificial Neural Networks, Kohonen T, Makisara K, Simula O, Kangas J (eds.), Elsevier, 1991, pp.397-402.

[11] Fritzke B. Growing cell structures | A self-organizing network for unsupervised and supervised learning. Neural Networks, 1994, 7(9): 1441-1460.

[12] Kohonen T. Self-organized formation of topologically correct feature maps. Biological Cybernetics, 1982, 43(1): 59-69.

[13] Hoppe H, DeRose T, Duchamp T, McDonald J, Stuetzle W. Mesh optimization. In Proc. Int. Conf. Computer Graphics and Interactive Techniques, Anaheim, USA, Aug. 2-6, 1993, pp.19-26.

[14] Eck M, DeRose T, Duchamp T, Hoppe H, Lounsbery M, Stuetzle W. Multiresolution analysis of arbitrary meshes. In Proc. Int. Conf. Computer Graphics and Interactive Techniques, Los Angeles, USA, Aug. 6-11, 1995, pp.173-182.

[15] Lee A, Sweldens W, Schrder P, Coswar L, Dobkin D. MAPS: Multiresolution adaptive parameterization of surfaces. In Proc. SlGGRAPH 98, Orlando, USA, July 19-24, 1998, pp.95-104.

[16] Alliez P, Meyer M, Desburn M. Interactivc geometry remeshing. ACM Transaction on Graphics, 2002, 21(3): 347-354.

[17] Alliez P, De Verdierc E C, Devillers O, Isenburg M. Isotropic surface remeshing. In Proc. SMI 2003, Seoul, Korea, May 12-15, 2003, p.49.

[18] Alvarez R, Noguera J V, Tortosa L, Zamora A. A mesh optimization algorithm based on neural networks. Journal of Information Sciences, 2007, 177(23): 5347-5364.

[19] Woodwark J. Blends in geometric modeling. In Proc. the Mathematics of Surfaces II., Cardiff, UK, 1987, pp.255-297.

[20] Sears K, Middleditch A. Blend surfaces for set-theoretic volume modeling systems. Computer Graphics, 1985, 19(3): 161-170.

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