A non-convex regularization method combined with Landweber method for image reconstruction in electrical resistance tomography

Shi, Yanyan; Li, Qifeng; Wang, Meng; Liu, Weina; Tian, Zhiwei

doi:10.1016/j.flowmeasinst.2021.101917

Cited by 11 publications

(3 citation statements)

References 36 publications

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“…Image reconstruction using ERT techniques requires the recovery of the conductivity distribution based on a finite number of measurements, which is a severely undefined inverse problem [36]. The Landweber method solves this problem by using the reconstruction results obtained in the LBP method as an initial greyscale continuously iterated [1,37]. The Landweber formula is as follows…”

Section: Image Reconstruction Based On the Lw-tdis Methodsmentioning

confidence: 99%

“…Electrical Resistance Tomography (ERT) is an imaging technique used for detection, with advantages such as fast response, low cost, no radiation, and non-intrusiveness [1][2][3]. It is widely applied in various industries including industrial inspection [4][5][6], archaeological research [7,8], biofilm analysis [9], and food industry [10,11].…”

Section: Introductionmentioning

confidence: 99%

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ERT image reconstruction using marker region segmentation method

Li,

Yang

2024

Meas. Sci. Technol.

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Inspired by image region segmentation method, a marking region segmentation iterative method is proposed to reconstruct sparse binary images of electrical resistance tomography. The grayscale matrix of the iteration process is mapped to another linear space for segmentation processing, adding the watershed thresholding of marking regions. In the iteration process for estimating conductivity distribution, the target regions are separated to avoid excessive segmentation effects. By applying this method in conjunction with the Landweber iterative model to solve the inverse problem of resistivity tomography imaging, more accurate binary images can be obtained, and it is easier to converge to the global optimal solution compared to the Landweber algorithm. To verify the reconstruction effectiveness of LW-TDIS method, numerical simulations and static experiments are conducted for comparison with three other methods. The results demonstrate that the proposed method effectively reduces reconstruction artifacts, improves reconstruction quality, and achieves better reconstruction performance.

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Section: Image Reconstruction Based On the Lw-tdis Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ERT image reconstruction using marker region segmentation method

Li,

Yang

2024

Meas. Sci. Technol.

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“…Based on the reconstructed images, location of the disease can be identified. Among these methods, regularization methods and iterative methods are very popular for image reconstruction [14][15][16]. In recent years, Bayesian learning based reconstruction methods have been presented [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Classification of Hemorrhage Using Priori Information of Electrode Arrangement With Electrical Impedance Tomography

et al. 2023

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Electrical impedance tomography is an emerging technique for brain disease detection. Generally, it requires that electrodes should be equidistantly placed around the detected region. However, this may be not possible for some patients who are undergoing post-surgical monitoring. Aiming at this problem, four kinds of non-uniform electrode arrangements are developed. To accurately detect the location of intracranial hemorrhage, a novel classification method based on a priori information of electrode arrangement is also proposed in this paper. According to the electrode arrangement information, the weight which corresponds to different kinds of electrode arrangement is separately determined during the training process. The proposed method is quantitatively evaluated with basic test dataset, test datasets under noise interruption, test datasets in the case of large contact impedance, test datasets with conductivity variation in different layers, and test datasets when considering modeling error and double inclusions. Comparisons with general classification methods are also conducted. The results show that the proposed method with residual network incorporated outperforms the classification methods of fully connected neural network and residual network. For all the test datasets, the results show that the accuracy is higher than 0.9 and the specificity reaches as high as 1 when the proposed method incorporating residual network is used.

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Improved electrical capacitance tomography algorithm based on homotopy perturbation regularization

Yan,

Liu

2023

Multimed Tools Appl

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A non-convex regularization method combined with Landweber method for image reconstruction in electrical resistance tomography

Cited by 11 publications

References 36 publications

ERT image reconstruction using marker region segmentation method

ERT image reconstruction using marker region segmentation method

Classification of Hemorrhage Using Priori Information of Electrode Arrangement With Electrical Impedance Tomography

Improved electrical capacitance tomography algorithm based on homotopy perturbation regularization

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