Artifact Optimization Algorithm for Pulmonary Electrical Impedance Tomography Based on Neighborhood Information and Fast FCM

DING Mingliang; LI Xiaotong; LU Lihui

doi:10.11999/JEIT210648

Volume 44 Issue 9

Sep. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(9): 3320-3327

DING Mingliang, LI Xiaotong, LU Lihui. Artifact Optimization Algorithm for Pulmonary Electrical Impedance Tomography Based on Neighborhood Information and Fast FCM[J]. Journal of Electronics & Information Technology, 2022, 44(9): 3320-3327. doi: 10.11999/JEIT210648

Citation:

DING Mingliang, LI Xiaotong, LU Lihui. Artifact Optimization Algorithm for Pulmonary Electrical Impedance Tomography Based on Neighborhood Information and Fast FCM[J]. Journal of Electronics & Information Technology, 2022, 44(9): 3320-3327. doi: 10.11999/JEIT210648

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Artifact Optimization Algorithm for Pulmonary Electrical Impedance Tomography Based on Neighborhood Information and Fast FCM

doi: 10.11999/JEIT210648

DING Mingliang^{1, 2
,
,},
LI Xiaotong¹,
LU Lihui^{1, 2}

1.
College of Engineering, Qufu Normal University, Rizhao 276826, China
2.
Rizhao Huilian Zhongchuang Institute of Intelligent Technology, Rizhao 276826, China

Funds: The National Natural Science Foundation of China (61973232), Shandong Natural Science Foundation (ZR2021MF083, ZR2019MEE054)

Received Date: 2021-06-29
Rev Recd Date: 2022-03-18

Available Online: 2022-04-13

Publish Date: 2022-09-19

Abstract

Abstract

To solve the problem of reconstruction image artifacts caused by the problem of "underdetermined" and the “soft field“ effect in the visualization process of electrical impedance tomography, an unsupervised image quality evaluation index based on neighborhood information and fast Fuzzy C-Means clustering (fast FCM) is proposed. Based on this evaluation index and Tikhonov regularization algorithm, a reconstruction image artifact optimization algorithm TR-NC is proposed. Simulation results show that the proposed algorithm can effectively correct artifacts in the reconstructed image, and the correlation coefficient of the modified reconstructed image has increased by 18.45% on average, and the relative error has reduced by 22.2% on average. Simulation experimental results show that the proposed algorithm can accurately detect the target when the change rate of target conductivity is more than 30%. It is shown that compared with the traditional Tikhonov regularization algorithm, the proposed modified algorithm TR-NC has been significantly improved in the number and position accuracy of reconstruction image targets, which provides a new imaging theoretical basis and technical reference for the application of electrical tomography technology to medical and industrial fields.
- Electrical impedance tomography,
- Image reconstruction,
- Fast fuzzy C-Means clustering,
- Neighborhood information,
- Artifact correction

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References(16)

References

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