Image reconstruction using a genetic algorithm for electrical capacitance tomography

Mou, Changhua; Peng, Lihui; Yao, Danya; Xiao, Di

doi:10.1016/s1007-0214(05)70123-1

Cited by 41 publications

(6 citation statements)

References 18 publications

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“…P. Lihue (2005) [ 10 ] developed a genetic algorithm for ECT image reconstruction. Algorithm initialization was based on linear back projection result.…”

Section: Soft Computingmentioning

confidence: 99%

A Survey of Soft Computing Approaches in Biomedical Imaging

Devi

Singh

Tiwari

et al. 2021

Journal of Healthcare Engineering

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Medical imaging is an essential technique for the diagnosis and treatment of diseases in modern clinics. Soft computing plays a major role in the recent advances in medical imaging. It handles uncertainties and improves the qualities of an image. Until now, various soft computing approaches have been proposed for medical applications. This paper discusses various medical imaging modalities and presents a short review of soft computing approaches such as fuzzy logic, artificial neural network, genetic algorithm, machine learning, and deep learning. We also studied and compared each approach used for other imaging modalities based on the certain parameter used for the system evaluation. Finally, based on comparative analysis, the possible research strategies for further development are proposed. As far as we know, no previous work examined this issue.

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“…P. Lihue (2005) [ 10 ] developed a genetic algorithm for ECT image reconstruction. Algorithm initialization was based on linear back projection result.…”

Section: Soft Computingmentioning

confidence: 99%

A Survey of Soft Computing Approaches in Biomedical Imaging

Devi

Singh

Tiwari

et al. 2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…To improve the quality of the reconstructed images, various image reconstruction algorithms have been developed over the years, such as the genetic algorithm [17], the Tikhonov regularization method [18], the Landweber iteration algorithm [19], the generalized vector sampled pattern matching method [20], the total variation regularization method [21,22], the simulated annealing algorithm [23], and algorithms based on neural networks [24], multi-objective functionals [25] and the extended Tikhonov regularization method [26]. Reports on the performances of other algorithms such as the algebraic reconstruction technique (ART) and the simultaneous iterative reconstruction technique (SIRT) can be found in [16].…”

Section: Ect Modelsmentioning

confidence: 99%

Preliminary study on ECT imaging of flames in porous media

Liu¹,

Chen²,

Xiong³

et al. 2008

Meas. Sci. Technol.

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This preliminary study for the first time investigated the feasibility of tomographic monitoring of flames in porous media, in which the cross-sectional profiles of flames inside a porous medium were imaged by electrical capacitance tomography (ECT). The relationship between the flame ionization and relative permittivity was established as the basis for ECT imaging of flames. Image reconstruction algorithms were discussed and an online iterative method OIOR was selected for image reconstruction. Experimental measurements were carried out and images of the flames were reconstructed. The shape, size and motion of the flames in a porous block were clearly monitored. Also the images correspond clearly to the variations of the combustion intensity. The feasibility of ECT monitoring of flames in porous media is proven by this study.

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“…To quantitatively evaluate the quality of reconstructed images, three criteria [9] were used: Relative image error: \\s -s\\ (13) Relative capacitance or potential residual:…”

Section: Figure 9 Potential and Noise Vectorsmentioning

confidence: 99%

Nonlinear Image Reconstruction Using a GA-ECT Technique in Electrical Capacitance Tomography

Zhou¹,

Xu²,

Yang³

et al. 2007

AIP Conference Proceedings

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Electrical capacitance tomography (ECT) is a non-invasive measurement technique that estimates the dielectric permittivity distribution of an inhomogeneous object from the boundary potentials at floating electrodes [1, 2]/or mutual capacitances. In this paper, a stochastic inverse technique based on genetic algorithm (GA-ECT) is developed, which is adapted to the two different methods, i.e. potential measurement and capacitance measurement. Numerical simulation results are presented to evaluate the inverse technique both for noise free and noisy data and the results show that quantitative image can be reconstructed not only with the low permittivity contrast but also with the high contrast. Furthermore, the influence of a priori knowledge to image reconstruction is discussed.Evaluation of the fitness according to object function © © © Crossover operation :P'(t)^crossover[P(t)] Mutation operation P"(t)^mutation[P'(t)]Similar to 3,4., evaluating the fitness of P"(t)

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Image reconstruction using a genetic algorithm for electrical capacitance tomography

Cited by 41 publications

References 18 publications

A Survey of Soft Computing Approaches in Biomedical Imaging

A Survey of Soft Computing Approaches in Biomedical Imaging

Preliminary study on ECT imaging of flames in porous media

Nonlinear Image Reconstruction Using a GA-ECT Technique in Electrical Capacitance Tomography

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