Research on Human Lung Impedance Tomography Based on Soft Thresholding Image Segmentation and Reduced-Order Tikhonov Regularization

The lung is one of the most vital organs in the human body, and its condition is closely correlated with overall health. Electrical impedance tomography (EIT), as a biomedical imaging technique, often produces low-quality reconstructed images due to its inherent ill-posedness in solving the inverse...

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Bibliographic Details
Published in:Applied sciences 2024-04, Vol.14 (8), p.3309
Main Authors: Song, Yang, Xiong, Lan, Liu, Zhenyou, Wu, Yongye, Zhang, Zhanlong
Format: Article
Language:English
Subjects:
Algorithms
Automation
biomedical imaging
Electric fields
electrical impedance tomography (EIT)
Image processing
image reconstruction
image segmentation
Inverse problems
Linear programming
Lung diseases
Mathematical models
Regularization methods
Sparsity
Tikhonov regularization
Tomography
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Summary:The lung is one of the most vital organs in the human body, and its condition is closely correlated with overall health. Electrical impedance tomography (EIT), as a biomedical imaging technique, often produces low-quality reconstructed images due to its inherent ill-posedness in solving the inverse problem. To address this issue, this paper proposes a soft-threshold region segmentation algorithm with a relaxation factor. This algorithm segments the reconstructed lung images into internal regions, edge regions, and background regions, resulting in clearer boundaries in the reconstructed images. This facilitates the intuitive identification of regions of interest by healthcare professionals. Additionally, this segmentation algorithm is suitably combined with a dimension-reduced Tikhonov regularization algorithm. By utilizing the joint capabilities of these algorithms, the partition points belonging to the background region can be excluded from the sought grayscale vector, thereby improving the ill-posedness of the image reconstruction process and enhancing the quality of image reconstruction. Finally, a 16-electrode human lung EIT simulation model is established for the thoracic region and verified through simulation. Experimental validation is conducted using a human lung tank simulation platform to further demonstrate the effectiveness of the proposed method.
ISSN:2076-3417
2076-3417
DOI:10.3390/app14083309