Determination of hyperparameter and similarity norm for electrical tomography algorithm using clustering validity index

Dong, Fanpeng; Yue, Shihong; Liu, Xuezhen; Wang, Huaxiang

doi:10.1016/j.measurement.2023.112976

Cited by 4 publications

(1 citation statement)

References 37 publications

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“…EIT has the advantages of safety, no radiation, fast response, portability, and low cost. It has received widespread attention and application in multiple fields including industrial measurement and biomedical detection, e.g., multi-phase flow visualization detection [ 4 , 5 , 6 ], bedside lung monitoring [ 7 , 8 ], and gesture capture [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Posterior Approximate Clustering-Based Sensitivity Matrix Decomposition for Electrical Impedance Tomography

Wang,

Sun,

2024

Sensors

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This paper introduces a sensitivity matrix decomposition regularization (SMDR) method for electric impedance tomography (EIT). Using k-means clustering, the EIT-reconstructed image can be divided into four clusters, derived based on image features, representing posterior information. The sensitivity matrix is then decomposed into distinct work areas based on these clusters. The elimination of smooth edge effects is achieved through differentiation of the images from the decomposed sensitivity matrix and further post-processing reliant on image features. The algorithm ensures low computational complexity and avoids introducing extra parameters. Numerical simulations and experimental data verification highlight the effectiveness of SMDR. The proposed SMDR algorithm demonstrates higher accuracy and robustness compared to the typical Tikhonov regularization and the iterative penalty term-based regularization method (with an improvement of up to 0.1156 in correlation coefficient). Moreover, SMDR achieves a harmonious balance between image fidelity and sparsity, effectively addressing practical application requirements.

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