Multiphase permittivity imaging using absolute value electrical capacitance tomography data and a level set algorithm

Hosani, Esra Al; Soleimani, Masoud

doi:10.1098/rsta.2015.0332

Cited by 16 publications

(5 citation statements)

References 19 publications

(30 reference statements)

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“…Its extension, color level set inversion schemes, have also proven to be useful as they sometimes depict more realistic scenarios. For example, a structural level set method was developed in [32] to detect tumours in breast tissue, a related approach was applied to industrial process tomography in [3], and similar schemes have been developed in the history matching of petroleum reservoirs [21]. Additional level set regularization techniques, such as parametric level sets, have also been used in other imaging modalities such as electrical impedance tomography, see [2,[38][39][40][41][42][43] for more information.…”

Section: Introductionmentioning

confidence: 99%

“…In order to do so, we consider a number of n s = 16 sources and n r = 16 receivers, with all sources and receivers representing wire loops as indicated as well in Figure 2. Each source and receiver has dimension [0.6] × [0.6] × [0.075]m 3 , with the sources being excited with an electric current I j = I = 0.1A and each receiver recording a data value represented by (3). A fixed and given probing frequency of f = 1MHz is applied in all cases.…”

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confidence: 99%

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Colour level set regularization for the electromagnetic imaging of highly discontinuous parameters in 3D

Hiles

Dorn

2020

Inverse Problems in Science and Engineering

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In this paper, we propose a novel reconstruction scheme for the low-frequency near-field electromagnetic imaging of high-contrast conductivity distributions inside shielded regions using the system of Maxwell's equations in 3D. In our novel scheme, we focus on estimating the shape characteristics of the electrical conductivity profile inside these regions from low-frequency electromagnetic data measured at external locations for a single frequency. We introduce a color level set regularization scheme which is a shape-based approach focusing on the simultaneous reconstruction of several shape-like distributions of different conductivity values in the same region of interest. We also introduce a topological perturbation scheme alongside the color level set regularization that is used to avoid a certain type of local minima which is characteristic for this simultaneous multi-value shape-based reconstruction. Using two numerical experiments focusing on a three-value reconstruction problem related to the imaging of shielded boxes or cargo containers, we compare this novel approach with results obtained from standard voxel-based reconstruction schemes on the one hand and the more established two-value shape based approach on the other hand. We demonstrate that, depending on the particular situation of the imaging setup, this three-value (or in general multiple-value) shape-based reconstruction technique has the potential to provide superior reconstruction results in many situations, in particular regarding reconstruction of the correct shapes. We also discuss particular challenges of this novel methodology.

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Colour level set regularization for the electromagnetic imaging of highly discontinuous parameters in 3D

Hiles

Dorn

2020

Inverse Problems in Science and Engineering

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“…Sensor technologies are mainly based on electrical tomography (ET) [34,35,36,37,38], which includes electrical capacitance tomography (ECT) [39,40,41,42,43,44,45] and electrical resistance tomography (ERT) [7,46,47]. It allows reconstruction of the image by the distribution of conductivity or permittivity of the object from electrical measurements at the edge of the object.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Selected Machine Learning Algorithms for Industrial Electrical Tomography

Rymarczyk

Kłosowski

Kozłowski

et al. 2019

Sensors

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The main goal of this work was to compare the selected machine learning methods with the classic deterministic method in the industrial field of electrical impedance tomography. The research focused on the development and comparison of algorithms and models for the analysis and reconstruction of data using electrical tomography. The novelty was the use of original machine learning algorithms. Their characteristic feature is the use of many separately trained subsystems, each of which generates a single pixel of the output image. Artificial Neural Network (ANN), LARS and Elastic net methods were used to solve the inverse problem. These algorithms have been modified by a corresponding increase in equations (multiply) for electrical impedance tomography using the finite element method grid. The Gauss-Newton method was used as a reference to machine learning methods. The algorithms were trained using learning data obtained through computer simulation based on real models. The results of the experiments showed that in the considered cases the best quality of reconstructions was achieved by ANN. At the same time, ANN was the slowest in terms of both the training process and the speed of image generation. Other machine learning methods were comparable with the deterministic Gauss-Newton method and with each other.

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“…Soleimani et al [25] employed the level-set method to control the quality of material and detect defects in solids using an ECT system. Later, Hosani and Soleimani [26] used the level-set method to detect multiple phases in a tomography system. Experimental measurements and numerical simulations showed that the proposed method is capable of estimating and identifying the phases in a multiphase process.…”

Section: Introductionmentioning

confidence: 99%

Image reconstruction of electrical capacitance tomography using the dynamic mesh and changing physical properties methods

Abbasian

Maddahian

2019

Meas. Sci. Technol.

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In this research, two new algorithms are introduced for image reconstruction of electrical capacitance tomography (ECT): the dynamic mesh (DM) and changing physical properties (CPP) algorithms. The presented algorithms estimate the boundary of an unknown object inside a circular domain. To capture the complex geometries, an unstructured mesh in conjunction with a finite volume method is employed. Several test cases are investigated to show the capability and reliability of these methods. The results show that the introduced methods need fewer measurements for accurate image reconstruction in comparison with previous methods. The number of iterations required for shape estimation and the accuracy of the DM method are better than the CPP method. Furthermore, the sensitivity analysis results show that by increasing the uncertainty of the measurements, the estimated shape has a slight deviation from the exact shape. The accuracy and speed of the introduced algorithms represent the new applicable algorithms for ECT image reconstruction.

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Multiphase permittivity imaging using absolute value electrical capacitance tomography data and a level set algorithm

Cited by 16 publications

References 19 publications

Colour level set regularization for the electromagnetic imaging of highly discontinuous parameters in 3D

Colour level set regularization for the electromagnetic imaging of highly discontinuous parameters in 3D

Comparison of Selected Machine Learning Algorithms for Industrial Electrical Tomography

Image reconstruction of electrical capacitance tomography using the dynamic mesh and changing physical properties methods

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