## 4 Discussion
These non-local algorithms utilize the intensity and relative distances of neighboring pixels to process images and achieve superior noise reduction compared with the MMWF [28]. We investigated whether the FNLM algorithm exhibited superior performance in segmentation tasks compared with the MMWF. Additional experiments were conducted by applying the median filter and the FNLM algorithm. In line with the evaluation methods employed in our study, we assessed the segmentation performance using the median filter and the FNLM algorithm. The results are presented in Figure 7. When using the median filter, the accuracy, F1-score, and IoU values were 0.9840, 0.9706, and 0.9430, respectively. In the case of the FNLM algorithm, these values were 0.9878, 0.9774, and 0.9559. When employing an MMWF, the corresponding values were 0.9960, 0.9928, and 0.9858. Accuracy, F1-score, and IoU were significantly improved when the MMWF was applied. To evaluate similarity while using the median  
Figure 6: Illustration of region-growing performance for lung segmentation using MMWF-based denoising with (**a**) 3 \(\times\) 3 and (**b**) optimized kernel size 5 \(\times\) 5.  
filter and the FNLM algorithm, we measured RMSE and PSNR, as shown in Figure 8. For the median filter, the RMSE and PSNR values were 0.1264 and 66.0961, respectively. When applying the FNLM algorithm, these values were 0.0366 and 76.8610. When the MMWF was applied, the values were 0.0271 and 79.4585. The highest similarity was observed when the MMWF was used. Evidently, improvement in segmentation performance does not necessarily correlate with noise removal performance. Furthermore, when calculating the time resolution, the median filter exhibited a value of 1.73 s, the MMWF showed 2.57 s, and the FNLM algorithm showed 28.92 s (Figure 9). MMWF demonstrated a time resolution improvement of 11.30 times over the FNLM algorithm. While the median filter provided the best time resolution, there was not a significant difference when compared to using the MMWF. Given that the MMWF outperforms significantly in terms of accuracy, F1-score, IoU, RMSE, and PSNR values, it is deemed most suitable for segmentation tasks. Figure 8: Results of RMSE and PSNR with median filter, FNLM algorithm, and MMWF. Figure 7: Accuracy, F1-score, and IoU results of the segmented image according to median filter, FNLM algorithm, and MMWF. In this study, our performance evaluation of segmentation employed metrics such as accuracy, F1-score, and IoU, with the assessment of similarity using RMSE and PSNR. However, evaluation metrics like asymmetric surface distance (ASSD), and relative volume difference (RVD) are also being employed for the evaluation of segmentation. The ASSD computes the difference in distance between the predicted segmentation image and the actual segmentation image using Euclidean distance. RVD represents the difference in volume between the predicted segmentation and the actual segmentation, primarily applied when predicting tumor sizes [29]. Thus, a variety of evaluation metrics exist, and it is suggested that future research should explore optimization strategies utilizing a broader range of evaluation factors. Furthermore, while this study focused on two-dimensional segmentation on individual slices, future evaluation on volume data should include three-dimensional segmentation across multiple slices. The segmentation performance of the method used in this study greatly depends on the location and selection method of the seed point initially set [30]. If the seed point is set incorrectly, the segmentation accuracy is drastically reduced. In addition, segmentation will not be performed properly if the boundaries of the area are ambiguous. The region-growing-based method is inaccurate, especially in area boundaries. Therefore, further research is needed on segmentation methods that can compensate for these shortcomings.