To generate a finite element model of human thorax using the VCH dataset

Hui Shi; Qian Liu

doi:10.1117/12.832780

30 October 2009 To generate a finite element model of human thorax using the VCH dataset

Hui Shi, Qian Liu

Proceedings Volume 7496, MIPPR 2009: Pattern Recognition and Computer Vision; 74960B (2009) https://doi.org/10.1117/12.832780
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

Purpose: To generate a three-dimensional (3D) finite element (FE) model of human thorax which may provide the basis of biomechanics simulation for the study of design effect and mechanism of safety belt when vehicle collision. Methods: Using manually or semi-manually segmented method, the interested area can be segmented from the VCH (Visible Chinese Human) dataset. The 3D surface model of thorax is visualized by using VTK (Visualization Toolkit) and further translated into (Stereo Lithography) STL format, which approximates the geometry of solid model by representing the boundaries with triangular facets. The data in STL format need to be normalized into NURBS surfaces and IGES format using software such as Geomagic Studio to provide archetype for reverse engineering. The 3D FE model was established using Ansys software. Results: The generated 3D FE model was an integrated thorax model which could reproduce human's complicated structure morphology including clavicle, ribs, spine and sternum. It was consisted of 1 044 179 elements in total. Conclusions: Compared with the previous thorax model, this FE model enhanced the authenticity and precision of results analysis obviously, which can provide a sound basis for analysis of human thorax biomechanical research. Furthermore, using the method above, we can also establish 3D FE models of some other organizes and tissues utilizing the VCH dataset.

Citation Download Citation

Hui Shi and Qian Liu "To generate a finite element model of human thorax using the VCH dataset", Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74960B (30 October 2009); https://doi.org/10.1117/12.832780

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