Image Reconstruction in Electrical Impedance Tomography through Multilayer Perceptron

Kouakouo Nomvussi, Serge Ayme; Mikulka, Jan

Image Reconstruction in Electrical Impedance Tomography through Multilayer Perceptron

Files

303-eeict-2024.pdf(979.68 KB)

Date

2024

Authors

Kouakouo Nomvussi, Serge Ayme

Mikulka, Jan

Publisher

Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií

Abstract

This study introduces a novel image reconstruction algorithm designed to excel in challenging scenarios with noisy datasets. Comparative evaluations against established methods, the Total Variation technique and the Gauss-Newton algorithm, are conducted using key performance metrics including the correlation coefficient and structural similarity index. The Results demonstrate that the proposed algorithm displays variable performance in noise-free data compared to Total Variation but consistently outperforms it in the presence of noise. Furthermore, when contrasted with the Gauss-Newton algorithm, the proposed method consistently exhibits superior outcomes, particularly in scenarios involving noisy datasets, where the Gauss-Newton algorithm faces limitations. This study underscores the robustness of the proposed algorithm in noisy conditions, suggesting its potential for applications where accurate image reconstruction is critical.

Keywords

Multilayer Perceptron, Total Variation, Newton- Gauss, EIT

Citation

Proceedings I of the 30st Conference STUDENT EEICT 2024: General papers. s. 303-307. ISBN 978-80-214-6231-1
https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_1.pdf

Document type

Peer-reviewed

Document version

Published version

Language of document

en

Document licence