## 7 Conclusions  
Electrical impedance tomography (EIT) often yields low-quality reconstructed images due to the inherent ill-posedness of the inverse problem. In this paper, image segmentation \begin{table}
\begin{tabular}{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 & 1.0731 & 0.7373 & 0.8063 & 0.7360 & 0.6707 & 0.7463 & 0.6335 & 0.5390 \\ ICC & 0.5048 & 0.8309 & 0.7748 & 0.8601 & 0.8936 & 0.8283 & 0.9160 & 0.9518 \\ SSIM & 2.8678 & 8.8574 & 7.4348 & 7.3229 & 8.3938 & 8.5889 & 10.3124 & 10.1009 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative table of image evaluation metrics for various algorithms for image reconstruction based on experimental data. Figure 12: Comparative evaluation of various algorithm metrics based on experimental data. techniques are introduced into EIT imaging, and a soft-threshold image segmentation method with a relaxation factor is proposed. The original image is segmented into three regions: the internal region, the edge region, and the background region. The selection of segmentation thresholds is based on optimizing the error norm between the boundary values of the segmented image and the true measured boundary impedance. By adjusting the relaxation factor, the degree of preservation of the edge region can be controlled. This segmentation method is used for the post-processing of lung reconstruction images in EIT. Furthermore, this soft-threshold image segmentation method effectively combines with the reduced-order Tikhonov algorithm by removing the discretization points belonging to the background region from the sought grayscale vector, thereby improving the ill-posedness issue in EIT. The main conclusions of this study are the following:  
1. Post-processing of lung EIT reconstruction images using image segmentation algorithms allows for effective identification of regions of interest, resulting in clear and distinguishable human lung images with well-defined edges. However, this process may lead to some information loss. In terms of evaluation metrics, the use of image segmentation algorithms reduces the relative error (IRE) and improves the image correlation coefficient (ICC) of the reconstructed images, but it may decrease the structural similarity index (SSIM). 2. Compared to fixed threshold segmentation methods, such as the maximum entropy threshold segmentation method, soft-threshold segmentation allows for adjusting the preservation degree of the edge region connected to the regions of interest and the background region. This widens the application scope of segmentation algorithms for EIT reconstruction by catering to different processing requirements. 3. The reduced-order Tikhonov algorithm addresses the ill-posedness issue in EIT by excluding the partition points belonging to the background region from the sought grayscale vector. The combination of image segmentation algorithms and the reduced-order Tikhonov algorithm effectively enhances the ill-posedness issue in image reconstruction, resulting in higher-quality reconstructed images. Finally, the effectiveness of the proposed algorithms was further demonstrated through numerical simulations and experimental verification using a 16-electrode human thoracic EIT simulation model and a human thoracic impedance tomography experimental platform. In summary, this paper introduces image segmentation algorithms into human lung electrical impedance tomography (EIT) technology, enabling the reconstruction of images that are closer to the real cross-sectional images of human lungs. Furthermore, these images facilitate easier recognition of lung contours by individuals. Conceptualization, Y.S. and L.X. ; methodology, Y.S. and Z.L. ; software, Y.S. ; validation, Y.S., L.X., and Z.L. ; formal analysis, Y.S. and Y.W. ; investigation, Y.S. and Y.W. ; resources, Z.Z. ; data curation, Z.Z. ; writing--original draft preparation, Y.S. ; writing--review and editing, Z.Z. ; visualization, L.X. ; supervision, Z.Z.