Optimization of Median Modified Wiener Filter for Improving Lung Segmentation Performance in Low-Dose Computed Tomography Images  
[PERSON]  
1 Department of Health Science, General Graduate School of Gachon University, 191, Hambakmoe-ro, Yeonsu-gu, Incheon 21936, Republic of Korea; [EMAIL_ADDRESS] (S.L.); [EMAIL_ADDRESS] (M.P.); [EMAIL_ADDRESS] (H.K.)  
[PERSON]  
1 Department of Health Science, General Graduate School of Gachon University, 191, Hambakmoe-ro, Yeonsu-gu, Incheon 21936, Republic of Korea; [EMAIL_ADDRESS] (S.L.); [EMAIL_ADDRESS] (M.P.); [EMAIL_ADDRESS] (H.K.)  
[PERSON]  
1 Department of Health Science, General Graduate School of Gachon University, 191, Hambakmoe-ro, Yeonsu-gu, Incheon 21936, Republic of Korea; [EMAIL_ADDRESS] (S.L.); [EMAIL_ADDRESS] (M.P.); [EMAIL_ADDRESS] (H.K.)  
[PERSON]  
2 Department of Biomedical Engineering, Euliji University, 533, Sanseong-daero, Sujung-gu, Seongnam-si 13135, Yeongong-do, Republic of Korea; [EMAIL_ADDRESS] (S.L.); [EMAIL_ADDRESS] (M.P.); [EMAIL_ADDRESS] (M.P.  
lung [5]. Furthermore, lung segmentation is essential for analyzing lung parenchymal density and quantitatively analyzing mechanisms related to the lung and diaphragm regions [6]. The region-growing-based segmentation algorithm is a commonly used approach for lung segmentation. It allows fast processing and exhibits excellent effectiveness in capturing subtle attenuation changes within tissues [7]. In addition, a region-growing-based segmentation algorithm can constrain a region by specifying an initial starting point. This capability allows for accurate region segmentation, even in areas without clear boundaries, such as the lungs [8].  
However, in LDCT, the inevitable presence of excessive noise poses challenges to achieving accurate segmentation [9]. The presence of white Gaussian noise exhibits a biologically irrelevant segmentation area, leading to over-segmentation and the representation of rounded tissue surfaces, such as the lungs, in spiky form [10; 11]. Additionally, as Gaussian noise increases, the variance between the pixel values of objects and noise increases, decreasing pixel-to-pixel similarity hindering proper region expansion when performing segmentation [12]. Moreover, when the section contains a large amount of noise with a negative value, no segmentation occurs.  
Various noise-removal filters such as Gaussian, median, and Wiener filters have been developed to enhance the segmentation accuracy. However, Gaussian filters cannot effectively remove noise types other than high-frequency noise [13]. In addition, the median filter erodes the distortion of spot edges, whereas the Wiener filter can induce blurring and spikes in the spots. To address these limitations and complement their characteristics, a fusion of the two filters, called the median modified Wiener filter (MMWF), has been proposed [14].  
The MMWF is an adaptive spatial filter, which relies on the kernel size as its sole parameter [15]. However, if the kernel size is too small, the noise may not be effectively removed, whereas a large kernel size can effectively remove noise but lead to pronounced blurring [16]. When excessive blurring occurs in an image, maintaining segmentation accuracy during the execution of region-growing-based segmentation poses a challenge. Setting the threshold values defensively is necessary to prevent over-segmentation but it can lead to a decrease in segmentation accuracy. Therefore, it is essential to derive an optimal kernel size that balances noise and blur for the effective application of the MMWF.  
The current study's goal was to apply an MMWF modeled by the kernel size to noisy lung CT images to determine the ideal MMWF kernel size for increasing the accuracy of lung segmentation. Subsequently, a comparative evaluation was conducted using quantitative evaluation factors.