Publication result detail

Image Reconstruction in Electrical Impedance Tomography through Multilayer Perceptron

KOUAKOUO NOMVUSSI, S.; MIKULKA, J.

Original Title

Image Reconstruction in Electrical Impedance Tomography through Multilayer Perceptron

English Title

Image Reconstruction in Electrical Impedance Tomography through Multilayer Perceptron

Type

Paper in proceedings outside WoS and Scopus

Original 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.

English 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.

Key words in English

Multilayer Perceptron, Total Variation, Newton-Gauss, EIT.

Authors

KOUAKOUO NOMVUSSI, S.; MIKULKA, J.

Released

23.04.2024

Publisher

VUT v Brně

Location

Brno

ISBN

978-80-214-6231-1

Book

Proceedings I of the 30th Conference STUDENT EEICT 2024

Pages from

303

Pages to

307

Pages count

5

URL

https://www.eeict.cz/download

BibTex

@inproceedings{BUT188847,
  author="Serge Ayme {Kouakouo Nomvussi} and Jan {Mikulka}",
  title="Image Reconstruction in Electrical Impedance Tomography through Multilayer Perceptron",
  booktitle="Proceedings I of the 30th Conference STUDENT EEICT 2024",
  year="2024",
  pages="303--307",
  publisher="VUT v Brně",
  address="Brno",
  isbn="978-80-214-6231-1",
  url="https://www.eeict.cz/download"
}