research-article

Optimization of the Radial Basis Function Neural Network Spread Factor for Electrical Impedance Tomography Image Reconstruction

Authors:

Marketa Venclikova,

Jakub Hlavica,

Michal Prauzek,

Jiri KoziorekAuthors Info & Claims

ICSPS 2016: Proceedings of the 8th International Conference on Signal Processing Systems

Pages 26 - 30

https://doi.org/10.1145/3015166.3015183

Published: 21 November 2016 Publication History

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Abstract

Electrical impedance tomography (EIT) is a low cost, non-invasive imaging technique where the inner resistivity distribution of the investigated object, corresponding to different tissue resistivity, is estimated from voltage measured on the boundary of the this object. The Electrical impedance tomography main problem is to get the resistivity distribution image of a given cross-sectional area based on the boundary voltage measurement. We used Radial basis function (RBF) neural network for image reconstruction in EIT and focused on examining the impact changing spread factor of the RBF to the results of the image reconstruction with the RBF neural network.

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Cited By

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Zhang TTian XLiu XYe JFu FShi XLiu RXu C(2022)Advances of deep learning in electrical impedance tomography image reconstructionFrontiers in Bioengineering and Biotechnology10.3389/fbioe.2022.101953110Online publication date: 14-Dec-2022
https://doi.org/10.3389/fbioe.2022.1019531
Badawy MIsmail NAlamrity S(2020)Implementation of Industrial Process Measurements Using Safe Tomography Techniques2020 8th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC)10.1109/JAC-ECC51597.2020.9355935(41-46)Online publication date: 14-Dec-2020
https://doi.org/10.1109/JAC-ECC51597.2020.9355935
Hrabuska RCedivodova VPrauzek MHlavica JKonecny J(2017)Electrical Impedance Distribution in Human Torax: A Modeling FrameworkProceedings of the Second International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’17)10.1007/978-3-319-68321-8_53(512-519)Online publication date: 30-Sep-2017
https://doi.org/10.1007/978-3-319-68321-8_53

Optimization of the Radial Basis Function Neural Network Spread Factor for Electrical Impedance Tomography Image Reconstruction
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches

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Published In

ICSPS 2016: Proceedings of the 8th International Conference on Signal Processing Systems

November 2016

235 pages

ISBN:9781450347907

DOI:10.1145/3015166

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 21 November 2016

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ICSPS 2016

ICSPS 2016: 8th International Conference on Signal Processing Systems

November 21 - 24, 2016

Auckland, New Zealand

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ICSPS 2016 Paper Acceptance Rate 46 of 83 submissions, 55%;

Overall Acceptance Rate 46 of 83 submissions, 55%

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Cited By

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Zhang TTian XLiu XYe JFu FShi XLiu RXu C(2022)Advances of deep learning in electrical impedance tomography image reconstructionFrontiers in Bioengineering and Biotechnology10.3389/fbioe.2022.101953110Online publication date: 14-Dec-2022
https://doi.org/10.3389/fbioe.2022.1019531
Badawy MIsmail NAlamrity S(2020)Implementation of Industrial Process Measurements Using Safe Tomography Techniques2020 8th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC)10.1109/JAC-ECC51597.2020.9355935(41-46)Online publication date: 14-Dec-2020
https://doi.org/10.1109/JAC-ECC51597.2020.9355935
Hrabuska RCedivodova VPrauzek MHlavica JKonecny J(2017)Electrical Impedance Distribution in Human Torax: A Modeling FrameworkProceedings of the Second International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’17)10.1007/978-3-319-68321-8_53(512-519)Online publication date: 30-Sep-2017
https://doi.org/10.1007/978-3-319-68321-8_53

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