## 4 Discussion
Furthermore, the region-growing-based segmentation method employed in this study exhibits limitations in cases of high anatomical variability within the organ or where diseases exist [31]. Particularly, it faces challenges when segmenting organs with ambiguous boundaries with surrounding structures. In scenarios with unclear organ boundaries, it becomes more sensitive to noise and blurring, making threshold setting difficult. Thus, additional optimization research is necessary to address these issues. There is a need to explore segmentation approaches other than region growing, such as model- and shape-based methods. Additionally, these image processing methods have been optimized for a single or limited number of images, which may limit their performance when handling large and variable volumes of images in clinical settings [23]. In addition, manual segmentation methods are time-consuming and rely on human interpretations and visual observation [29]. Hence, there is a potential risk of misdiagnosis in certain segmented regions as disease or tumors. Therefore, future research should focus on enhancing performance by establishing extensive CT image databases in the field of medical image analysis through the application of the MMWF approach. Utilizing deep learning techniques like convolutional neural networks and U-Net for automated segmentation methods rather than manual approaches becomes essential. The MMWF is expected to be used as a pre-processing algorithm for image augmentation based on fast computation times in deep learning. Furthermore, such network construction can be applied not only to lung segmentation, but also to various  
Figure 9: Results of time resolution with median filter, FNLM algorithm, and MMWF. other areas' segmentation, such as cortical bone, which is difficult to segment due to the intricate distinction between marrow and bone in the periosteal boundary and connective tissue and bone in the endosteal boundary [32]. Various preprocessing techniques are available to enhance segmentation performance. In addition to noise removal, techniques such as window adjustment, resolution enhancement, data standardization, and histogram equalization are employed [29]. Windowing is utilized to emphasize regions of interest in an image and remove unnecessary information, and is often applied to visualize specific areas more effectively by configuring appropriate window width and level settings. Resolution enhancement aims to sharpen indistinct boundaries and enable detailed observation, thus improving the spatial or temporal resolution of an image. Typically, resolution enhancement is achieved through techniques such as super-resolution, interpolation, and deep learning. Data standardization involves transforming pixel values in an image to a standard scale, enhancing the stability of segmentation algorithms. By adjusting the mean and standard deviation of the data, all images can be represented within a consistent range of pixel intensities. Histogram equalization adjusts the pixel intensity distribution of an image, enhancing the contrast between dark and bright regions. This process can improve boundary detection during segmentation. Consequently, in addition to noise removal, various preprocessing steps are performed, and optimization studies for each preprocessing step are deemed necessary. Recently, no-reference-based parameters are being used as a method to evaluate the degree of improvement in medical image quality. Natural image quality evaluator (NIQE) and blind image spatial quality evaluator (BRISQUE) evaluation methods are used to measure the noise performance of medical images and have the advantage of quantitatively analyzing the overall image quality [33; 34]. The factors that analyze the form of natural scene statistics in the acquired medical image are NIQE and BRISQUE evaluation parameters, and the principle of deriving these values is to analyze the degree of distortion of the shape of the Gaussian curve.