Logistic regression in image reconstruction in electrical impedance tomography

Kozłowski, Edward

doi:10.15199/48.2020.05.19

Cited by 3 publications

(2 citation statements)

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“…In this paper, logistic regression has been used to create a classifier. The logistic regression model is used to estimate the binomial (or multinomial) distribution of the response variable Y based on the realisation of the input variables X:R m (in other words, we determine P(Y = y|X), where y {0,1}).In the literature P(Y = 1|X) value denotes the success probability, but P(Y = 0|X)defeat probability [14,[16][17]27].…”

Section: Logistic Regressionmentioning

confidence: 99%

Sensor Platform of Industrial Tomography for Diagnostics and Control of Technological Processes

Król¹,

Rymarczyk²,

Niderla³

et al. 2023

IAPGOS

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This article presents an industrial tomography platform used to diagnose and control technological processes. The system has been prepared so that it is possible to add individual sensors cooperating with the system of an intelligent cyber-physical platform with an open architecture. Additionally, it is possible to configure and cooperate with external systems freely. As part of the experimental work, a platform has been developed that allows individual subsystems and external customer systems to work together. The cyber-physical system, a new generation of digital systems, focuses mainly on the complex interplay and integration between cyberspace and the physical world. A cyber-physical system consists of highly integrated computational, communication, control and physical elements. The solution focuses mainly on the complex interplay and integration between cyberspace and the physical world.

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Section: Logistic Regressionmentioning

confidence: 99%

Sensor Platform of Industrial Tomography for Diagnostics and Control of Technological Processes

Król¹,

Rymarczyk²,

Niderla³

et al. 2023

IAPGOS

Self Cite

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“…Several machine-learning methods for EIT reconstruction have been discussed in the literature. In the article [18], the authors explored logistic regression using an elastic net. The paper [16] used neural networks for EIT image reconstruction, where N neural networks trained separately for each output pixel were used to reconstruct image pixel values.…”

Section: Introductionmentioning

confidence: 99%

Novel Hybrid Algorithm Using Convolutional Autoencoder With SVM for Electrical Impedance Tomography and Ultrasound Computed Tomography

Maciura¹,

Wójcik²,

Rymarczyk³

et al. 2023

IAPGOS

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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

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3D modeling of the urinary bladder using electrical impedance tomography: advanced reconstruction algorithms and medical applications

Stefańczak,

Dziadosz,

Iskra

et al. 2024

JoMS

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Purpose: The research presented in this paper was conducted to obtain a detailed 3D model of the urinary bladder using electrical impedance tomography, a noninvasive tomographic technique. Methods: Electrical impedance tomography (EIT) is an imaging technique that measures electrical impedance inside the human body. Many methods, including those based on physical models and machine learning, are used to reconstruct the considered 3D object using EIT. The work focuses on the Gauss-Newton algorithm in its generalized form. Results: Three-dimensional reconstructions of the urinary bladder were obtained. The models are built with high accuracy and can be processed by subsequent algorithms. Discussion: The constructed models can serve as the basis for correct diagnosis in medicine and as research material for subsequent work, for example, on the possibilities of 3D printing. Possible methods of obtaining even higher-quality reconstruction also remain to be considered.

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Logistic regression in image reconstruction in electrical impedance tomography

Cited by 3 publications

References 0 publications

Sensor Platform of Industrial Tomography for Diagnostics and Control of Technological Processes

Sensor Platform of Industrial Tomography for Diagnostics and Control of Technological Processes

Novel Hybrid Algorithm Using Convolutional Autoencoder With SVM for Electrical Impedance Tomography and Ultrasound Computed Tomography

3D modeling of the urinary bladder using electrical impedance tomography: advanced reconstruction algorithms and medical applications

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