Biomedical subjects

Bijan Shirinzadeh

Publications and source records attributed to Bijan Shirinzadeh.

3 recordsLinked to original sources

PubMed2006

Soft tissue modelling through autowaves for surgery simulation.

Modelling of soft tissue deformation is of great importance to virtual reality based surgery simulation. This paper presents a new methodology for simulation of soft tissue deformation by drawing an analogy between autowaves and soft tissue deformation. The potential energy stored in a soft tissue as a result of a deformation caused by an external force is propagated among mass points of the soft tissue by non-linear autowaves. The novelty of the methodology is that (i) autowave techniques are established to describe the potential energy distribution of a deformation for extrapolating internal forces, and (ii) non-linear materials are modelled with non-linear autowaves other than geometric non-linearity. Integration with a haptic device has been achieved to simulate soft tissue deformation with force feedback. The proposed methodology not only deals with large-range deformations, but also accommodates isotropic, anisotropic and inhomogeneous materials by simply changing diffusion coefficients.

Biomimetics↗

PubMed2006

Simulation of deformable models with the Poisson equation.

In this paper, we present a new methodology for the deformation of soft objects by drawing an analogy between the Poisson equation and elastic deformation from the viewpoint of energy propagation. The potential energy stored due to a deformation caused by an external force is calculated and treated as the source injected into the Poisson system, as described by the law of conservation of energy. An improved Poisson model is developed for propagating the energy generated by the external force in a natural manner. An autonomous cellular neural network (CNN) model is established by using the analogy between the Poisson equation and CNN to solve the Poisson model for the real-time requirement of soft object deformation. A method is presented to derive the internal forces from the potential energy distribution. The proposed methodology models non-linear materials with the non-linear Poisson equation and thus non-linear CNN, rather than geometric non-linearity. It not only deals with large-range deformations, but also accommodates isotropic, anisotropic and inhomogeneous materials by simply modifying constitutive coefficients. A haptic virtual reality system has been developed for deformation simulation with force feedback. Examples are presented to demonstrate the efficiency of the proposed methodology.

Algorithms↗

PubMed2006

A cellular neural network methodology for deformable object simulation.

This paper presents a new methodology to simulate soft object deformation by drawing an analogy between a cellular neural network (CNN) and elastic deformation. The potential energy stored in an elastic body as a result of a deformation caused by an external force is propagated among mass points by a nonlinear CNN. The novelty of the methodology is that: 1) CNN techniques are established to describe the potential energy distribution of the deformation for extrapolating internal forces and 2) nonlinear materials are modeled with nonlinear CNNs rather than geometric nonlinearity. Integration with a haptic device has been achieved for deformable object simulation with force feedback. The proposed methodology not only predicts the typical behaviors of living tissues, but it also accommodates isotropic, anisotropic, and inhomogeneous materials, as well as local and large-range deformation.

Biomimetics↗