## 5 Numerical Simulation and Experimental Verification
### Numerical Simulation Verification  
To validate the effectiveness of the soft-threshold region segmentation algorithm and its compatibility with the reduced-order Tikhonov regularization algorithm, a 16-electrode EIT simulation model of the human thoracic field was established. Numerical simulations were conducted using commonly used image reconstruction algorithms, including the local binary patterns (LBP) algorithm, the Landweber algorithm, and the classical Tikhonov regularization algorithm. A comparative analysis of the imaging results was performed using the maximum entropy threshold segmentation algorithm (METS) and a single reduced-order Tikhonov regularization algorithm (RO-TK). Numerical simulations were also conducted for the METS, soft-threshold segmentation algorithm (STS), reduced-order Tikhonov regularization algorithm (RO-TK), segmentation-reduced-order Tikhonov regularization algorithm (STS-RO-TK), and segmentation-reduced-order-segmentation Tikhonov regularization algorithm (STS-RO-STS-TK). The relationship between the abbreviations and the full names of the algorithms involved in this paper can be found in Table 1 in Appendix A. The initial images for the METS and STS algorithms were obtained using the reconstruction images from the Tikhonov regularization algorithm. The resulting comparative images of the various algorithms for image reconstruction are shown in Figure 8.  
By observing the image reconstruction results, it can be noticed that the LBP algorithm only approximates the imaging target, and the image boundaries are very blurry. Moreover, its performance in reconstructing the left and right lung images is poor. The Landweber algorithm and the Tikhonov algorithm are able to reconstruct the left and right lung regions more distinctly, but the image boundaries are not sharp enough. Using self-used threshold segmentation or maximum entropy threshold segmentation algorithms can clearly define the boundary of the region of interest, but the reconstructed image shapes deviate from the real distribution. In contrast, the reconstructed image obtained using the reduced-order Tikhonov regularization can better depict the details of the image boundaries compared to traditional Tikhonov regularization, resulting in a closer representation of the actual medium distribution. Furthermore, applying soft-threshold region segmentation on the reconstructed image obtained using reduced-order Tikhonov regularization can reduce image artifacts within the internal region and further clarify the lung regions of interest. Evaluation of image reconstruction results for various imaging algorithms discussed in this paper was conducted using the image evaluation metric calculation method provided in the previous section. The results of the evaluation metrics for the image reconstruction results obtained based on measurements from the simulation model are shown in Table 1 and Figure 9. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **LBP** & **Landweber** & **Tikhonov** & **METS** & **STS** & **RO-TK** & **STS-RO-TK** & **STS-RO-STS-TK** \\ \hline IRE & 0.9369 & 0.7024 & 0.7534 & 0.7273 & 0.6713 & 0.7275 & 0.6296 & 0.5683 \\ ICC & 0.6018 & 0.8490 & 0.8061 & 0.8480 & 0.8839 & 0.8311 & 0.9128 & 0.9355 \\ SSIM & 3.6395 & 6.7307 & 6.0299 & 5.4428 & 6.0037 & 6.3347 & 7.2632 & 7.1012 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative evaluation of reconstructed image metrics based on simulated data for various algorithms. Figure 8: Comparative reconstructed images of various algorithms based on simulated data. The analysis of the image evaluation metrics leads to the following conclusions: the use of image segmentation algorithms can reduce the relative error (IRE) of the reconstructed images and improve the image correlation coefficient (ICC). However, it may result in a certain degree of decrease in the structural similarity index (SSIM) of the images.