Electrical impedance tomography: A reconstruction method based on neural networks and particle swarm optimization

SéBastien Martin, Charles T. M. Choi*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Electrical Impedance Tomography (EIT) is a non-invasive image reconstruction technique. Typically, an EIT scheme involves the solution to an inverse problem, which usually gives a poor resolution, due to linearization and ill-posedness of the problem. An alternative approach based on Artificial Neural Networks (ANN) has been used as a replacement of the inverse problem, giving correct results without linearizing the problem. However, training an ANN may be time consuming and usually requires a large amount of iterations before achieving a correct answer to the input stimulation. Several studies focused on training ANNs, and Evolutionary Algorithms (EA) gives a faster global convergence. In this paper, a novel approach based on Artificial Neural Networks and Particle Swarm Optimization (PSO) is proposed to improve the training process. A training method based on PSO algorithm achieves a faster global convergence.

Original languageEnglish
Title of host publication1st Global Conference on Biomedical Engineering and 9th Asian-Pacific Conference on Medical and Biological Engineering
EditorsShyh-Hau Wang, Fong-Chin Su, Ming-Long Yeh
PublisherSpringer Verlag
Pages177-179
Number of pages3
ISBN (Electronic)9783319122618
DOIs
StatePublished - 1 Jan 2015
Event1st Global Conference on Biomedical Engineering, GCBME 2014 and 9th Asian-Pacific Conference on Medical and Biological Engineering, APCMBE 2014 - Tainan, Taiwan
Duration: 9 Oct 201412 Oct 2014

Publication series

NameIFMBE Proceedings
Volume47
ISSN (Print)1680-0737

Conference

Conference1st Global Conference on Biomedical Engineering, GCBME 2014 and 9th Asian-Pacific Conference on Medical and Biological Engineering, APCMBE 2014
CountryTaiwan
CityTainan
Period9/10/1412/10/14

Keywords

  • Electrical impedance tomography
  • Finite element method
  • Inverse problems
  • Neural network
  • Particle swarm optimization

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