Hopfield neural network for modeling of soft tissue deformation

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 def...

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Bibliographic Details
Main Authors: Zhong, Yongmin, Gu, C., Shirinzadeh, B., Smith, J.
Other Authors: Ian Howard
Format: Conference Paper
Published: Engineers Australia 2010
Subjects:
Online Access:http://hdl.handle.net/20.500.11937/33362
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author Zhong, Yongmin
Gu, C.
Shirinzadeh, B.
Smith, J.
author2 Ian Howard
author_facet Ian Howard
Zhong, Yongmin
Gu, C.
Shirinzadeh, B.
Smith, J.
author_sort Zhong, Yongmin
building Curtin Institutional Repository
collection Online Access
description 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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format Conference Paper
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institution Curtin University Malaysia
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last_indexed 2025-11-14T08:32:15Z
publishDate 2010
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spelling curtin-20.500.11937-333622017-01-30T13:36:43Z Hopfield neural network for modeling of soft tissue deformation Zhong, Yongmin Gu, C. Shirinzadeh, B. Smith, J. Ian Howard Hopfield neural network mechanical load soft tissue deformation and neural dynamics 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. 2010 Conference Paper http://hdl.handle.net/20.500.11937/33362 Engineers Australia restricted
spellingShingle Hopfield neural network
mechanical load
soft tissue deformation
and neural dynamics
Zhong, Yongmin
Gu, C.
Shirinzadeh, B.
Smith, J.
Hopfield neural network for modeling of soft tissue deformation
title Hopfield neural network for modeling of soft tissue deformation
title_full Hopfield neural network for modeling of soft tissue deformation
title_fullStr Hopfield neural network for modeling of soft tissue deformation
title_full_unstemmed Hopfield neural network for modeling of soft tissue deformation
title_short Hopfield neural network for modeling of soft tissue deformation
title_sort hopfield neural network for modeling of soft tissue deformation
topic Hopfield neural network
mechanical load
soft tissue deformation
and neural dynamics
url http://hdl.handle.net/20.500.11937/33362