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    Hopfield neural network for modeling of soft tissue deformation

    Access Status
    Fulltext not available
    Authors
    Zhong, Yongmin
    Gu, C.
    Shirinzadeh, B.
    Smith, J.
    Date
    2010
    Type
    Conference Paper
    
    Metadata
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    Citation
    Zhong, Yongmin and Gu, Chengfan and Shirinzadeh, Bijian and Smith, Julian. 2010. Hopfield neural network for modeling of soft tissue deformation, in Howard, I. (ed), 6th Australian Congress on Applied Mechanics, Dec 12 2010. Perth, WA: Engineers Australia.
    Source Title
    Proceedings of 6th Australian Congress on Applied Mechanics
    Source Conference
    6th Australian Congress on Applied Mechanics
    School
    Department of Mechanical Engineering
    URI
    http://hdl.handle.net/20.500.11937/33362
    Collection
    • Curtin Research Publications
    Abstract

    This paper presents a new methodology for soft tissue deformation by using neural dynamics. This methodology combines neural propagation of mechanical load and non-rigid mechanics of motion to govern the dynamics of soft tissue deformation. The mechanical load applied to a soft tissue to cause a deformation is treated as the input of neural network and distributed among mass points of the soft tissue according to neural dynamics. A Hopfield neural network model is developed to describe the distribution of the mechanical load in the tissue. Methods are established for construction of the neural network model on a 3D tissue surface and for derivation of internal forces from the distribution of the mechanical load. Experiments have been conducted, demonstrating that the proposed methodology cannot only deal with large-range deformation, but it can also accommodate isotropic, anisotropic and inhomogeneous materials by simply modifying the control coefficient.

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