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Image reconstruction algorithms for electrical impedance tomography based on swarm intelligence

Image reconstruction algorithms for electrical impedance tomography based on swarm intelligence

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Swarm intelligence algorithms have a wide range of application possibilities such as in the fields of engineering, economics, biology and medicine. One technique that emerges from the use of such algorithms is the electrical impedance tomography (EIT), which consists of a noninvasive and free of ionizing radiation imaging technique with applications in industry, geophysics and medicine. The technique is based on the application of a pattern of alternating low amplitude and high-frequency electric current through electrodes arranged around the surface of the section of the object to be imaged and in the consequent analysis of the electric potential measured by others electrodes. EIT technique consists of the solution to the direct and inverse problems. The direct problem consists in defining the electrical potentials within the object section and the potentials measured in its boundary by knowing the internal conductivity distribution of the object and the current excitation pattern, the relation of which is given by Laplace's equation. However, the estimation of the conductivity and electrical permittivity distribution of the interior of the body section from the measurements of the excitation response is mathematically an inverse, nonlinear and ill-posed problem. These characteristics make their solution quite dependent on the reconstruction and regularization algorithm and can be obtained through noniterative and interactive methods. The proposal of this chapter is to present the development of a low-cost hardware-software electrical impedance tomography system employing a project-partitioning strategy, developed for the acquisition and conditioning of data to preprocess and transfer the electrical potentials from the measured boundary of the object to a computer, performing the image reconstruction with swarm-based algorithms, namely, particle swarm optimization (PSO), artificial bee colony and fish school search.

Chapter Contents:

  • Abstract
  • 2.1 Introduction
  • 2.2 Related work
  • 2.3 Swarm intelligence
  • 2.3.1 Particle swarm optimization
  • 2.3.2 Fish school search
  • 2.3.2.1 Individual movement operator
  • 2.3.2.2 Feeding operator
  • 2.3.2.3 Collective-instinctive movement operator
  • 2.3.2.4 Collective-volitive movement operator
  • 2.3.3 Artificial bee colony
  • 2.4 Application: electrical impedance tomography
  • 2.4.1 Mathematical modeling of EIT problems
  • 2.4.2 Direct and inverse problem
  • 2.4.2.1 Direct problem
  • 2.4.2.2 Inverse problem
  • 2.4.3 EIT reconstruction method as an optimization problem
  • 2.4.4 Ground-truth images
  • 2.4.5 Computational solutions obtained from swarm techniques
  • 2.4.6 Hardware proposal
  • 2.4.6.1 The embedded control system
  • References

Inspec keywords: search problems; particle swarm optimisation; medical image processing; electric impedance imaging; electric potential; electric current; inverse problems; image reconstruction; permittivity; Laplace equations

Other keywords: particle swarm optimization; current excitation pattern; direct problems; regularization algorithm; internal conductivity distribution; image reconstruction algorithms; inverse problems; electrical potentials; artificial bee colony; EIT technique; electrical impedance tomography system; electrical permittivity distribution; fish school search; radiation imaging technique; PSO; swarm intelligence algorithms

Subjects: Optimisation techniques; Mathematical analysis; Mathematical analysis; Biology and medical computing; Optimisation techniques; Optical, image and video signal processing; Bioelectric signals; Computer vision and image processing techniques

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