NOVEL HYBRID ALGORITHM USING CONVOLUTIONAL AUTOENCODER WITH SVM FOR ELECTRICAL IMPEDANCE TOMOGRAPHY AND ULTRASOUND COMPUTED TOMOGRAPHY

Łukasz Maciura; Dariusz Wójcik; Tomasz Rymarczyk; Krzysztof Król

doi:10.35784/iapgos.3377

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Published: 2023-06-30

DOI: https://doi.org/10.35784/iapgos.3377

Issue Vol. 13 No. 2 (2023)

NOVEL HYBRID ALGORITHM USING CONVOLUTIONAL AUTOENCODER WITH SVM FOR ELECTRICAL IMPEDANCE TOMOGRAPHY AND ULTRASOUND COMPUTED TOMOGRAPHY
Łukasz Maciura, Dariusz Wójcik, Tomasz Rymarczyk, Krzysztof Król

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86-92

Authors

Łukasz Maciura

lukasz.maciura@netrix.com.pl

Research and Development Center, Netrix S.A,. Lublin, Poland

http://orcid.org/0000-0001-8657-3472 (unauthenticated)

Dariusz Wójcik

dariusz.wojcik@netrix.com.pl

Research and Development Center, Netrix S.A,. Lublin, Poland

http://orcid.org/0000-0002-4200-3432 (unauthenticated)

Tomasz Rymarczyk

tomasz.rymarczyk@netrix.com.pl

Research and Development Center, Netrix S.A,. Lublin, Poland

http://orcid.org/0000-0002-3524-9151 (unauthenticated)

Krzysztof Król

krzysztof.krol@netrix.com.pl

Research and Development Center, Netrix S.A,. Lublin, Poland

http://orcid.org/0000-0002-0114-2794 (unauthenticated)

Abstract

This paper presents a new hybrid algorithm using multiple Support Vector Machines models with convolutional autoencoder to Electrical Impedance Tomography, and Ultrasound Computed Tomography image reconstruction. The ultimate hybrid solution uses multiple SVM models to convert input measurements to individual autoencoder codes representing a given scene then the decoder part of the autoencoder can reconstruct the scene

Keywords:

convolutional autoencoder, SVM, electrical impedance tomography, ultrasound transmission tomography

References

Aziz Taha A., Hanbury A.: Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Medical Imaging 15(29), 2015, 1–28.

Chen B. et al.: Extended Joint Sparsity Reconstruction for Spatial and Temporal ERT Imaging. Sensors 18, 2018, 4014.

Chen P. H. et al.: A tutorial on ν-support vector machines. Applied Stochastic Models in Business and Industry 21, 2005, 111–136.

Chen Z. et al.: Application of Deep Neural Network to the Reconstruction of Two-Phase Material Imaging by Capacitively Coupled Electrical Resistance Tomography. Electronics 10, 2021, 1058.

Duraj A., Korzeniewska E., Krawczyk A.: Classification algorithms to identify changes in resistance. Przegląd Elektrotechniczny 91(12), 2015, 82–84.

Dusek J., Mikulka J.: Measurement-Based Domain Parameter Optimization in Electrical Impedance Tomography Imaging. Sensors 21, 2021, 2507.

Fan Y. et al.: DDN: dual domain network architecture for non-linear ultrasound transmission tomography reconstruction. Proc. SPIE 11602, 2021, 1160209 [http://doi.org/10.1117/12.2580911].

Fan Y., Ying L.: Solving electrical impedance tomography with deep learning. Journal of Computational Physics 404, 2020, 109119.

Fernandez-Fuentes X. et al.: Towards a Fast and Accurate EIT Inverse Problem Solver: A Machine Learning Approach. Electronics 7(12), 2018, 422.

Hamilton S. J., Hauptmann A.: Deep D – bar: Real time Electrical Impedance Tomography Imaging with Deep Neural Networks. IEEE Trans. Med. Imaging 37(10), 2018, 2367–2377.

Józefczak A. et al.: Ultrasound transmission tomography-guided heating with nanoparticles. Measurement 197, 2022, [http://doi.org/10.1016/j.measurement.2022.111345].

Kania K. et al.: Image reconstruction in ultrasound transmission tomography using the Fermat's Principle. Przegląd Elektrotechniczny 96(1), 2020, 186–189.

Khan T. A., Ling S.H.: Review on Electrical Impedance Tomography: Artificial Intelligence Methods and its Applications. Algorithms 12(5), 2019, 1–18.

Kłosowski G. et al.: Comparison of Machine Learning Methods for Image Reconstruction Using the LSTM Classifier in Industrial Electrical Tomography. Energies 14(21), 2021, 7269.

Kłosowski G. et al.: Maintenance of industrial reactors supported by deep learning driven ultrasound tomography. Przegląd Elektrotechniczny 98(4), 2022, 138–147.

Kłosowski G. et al.: Neural hybrid tomograph for monitoring industrial reactors, Przegląd Elektrotechniczny 96(12), 2020, 190–193.

Kłosowski G. et al.: Quality Assessment of the Neural Algorithms on the Example of EIT-UST Hybrid Tomography. Sensors 20(11), 2020, 3324.

Kozłowski E. et al.: Logistic regression in image reconstruction in electrical impedance tomography, Przegląd Elektrotechniczny 96(5), 2020, 95–98.

Krawczyk A., Korzeniewska E.: Magnetophosphenes–history and contemporary implications. Przegląd Elektrotechniczny 94(1), 2018, 61–64.

Li X. et al.: An image reconstruction framework based on deep neural network for electrical impedance tomography. IEEE International Conference on Image Processing, Beijing, China, 2017.

Li X. et. al.: A novel deep neural network method for electrical impedance tomography. Transactions of the Institute of Measurement and Control 41(14), 2019, 4035–4049.

Łukiański M., Wajman R.: The diagnostic of two-phase separation process using digital image segmentation algorithms. Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 10(3), 2020, 5–8.

Mosorov V. et al.: Plug Regime Flow Velocity Measurement Problem Based on Correlability Notion and Twin Plane Electrical Capacitance Tomography: Use Case. Sensors 21(6), 2021, 2189 [http://doi.org/10.3390/s21062189].

Seo J. K. et al.: A Learning – Based Method for Solving III – Posed Nonlinear Inverse Problems: A Simulation Study of Lung EIT, SIAM. Journal on Imaging Sciences 12(3), 2019.

Szczesny A., Korzeniewska E.: Selection of the method for the earthing resistance measurement. Przegląd Elektrotechniczny 94, 2018, 178–181.

Yu H., Kim S.: SVM Tutorial: Classification, Regression, and Ranking. Handbook of Natural computing, 2012.

Zhao W. et al.: Ultrasound transmission tomography image reconstruction with a fully convolutional neural network. Phys Med Biol. 65(23), 2020, 235021, [http://doi.org/10.1088/1361-6560/abb5c3. PMID: 33245050].

NOVEL HYBRID ALGORITHM USING CONVOLUTIONAL AUTOENCODER WITH SVM FOR ELECTRICAL IMPEDANCE TOMOGRAPHY AND ULTRASOUND COMPUTED TOMOGRAPHY. (2023). Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 13(2), 4-9. https://doi.org/10.35784/iapgos.3377

NOVEL HYBRID ALGORITHM USING CONVOLUTIONAL AUTOENCODER WITH SVM FOR ELECTRICAL IMPEDANCE TOMOGRAPHY AND ULTRASOUND COMPUTED TOMOGRAPHY

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