## 3 Results and Discussion
### Post-Multiply FV Method
The 2D gamma-CT images from L\({}_{\mathit{off}}\) 15 to L\({}_{\mathit{off}}\) 20, including the \({}^{206}\)Pb and \({}^{208}\)Pb rods in addition to the vacant area, were aligned with the 2D NRF\({}^{\sim}\) - CT image of L\({}_{\mathit{NRF}}\)18. 3. 2D Gamma-CT Image Segmentation  
Image segmentation is a process of partitioning an image into numerous segments or a process of placing pixels such that they have non-overlapping areas [23]. This segmentation procedure is the first stage of image analysis [24], object representation, visualization, and other image processing strategies used in a variety of fields [25]. The main goal of image segmentation is to simplify and/or transform an image into one that can be readily analyzed [24]. In the case of segmentation of a 2D gamma-CT image, we assumed that the atomic absorption intensities varied in some regions of the CT sample and that the intensity at each pixel in a region where a rod existed would be almost constant. The first step of 2D gamma-CT image segmentation was primary image scaling. We scaled the primary gamma-CT images to a value between 0 and 255. The range of 0 to 255 was chosen to provide for an 8-bit representation of each pixel. The black, gray, and white colors were represented by values of 0, 128, and 255, respectively. One of the most commonly used approaches used for image segmentation is the threshold method [24,26]. Thresholding plays a crucial role in various algorithms used for image analysis, object representation, and visualization [27]. In this method, we chose a threshold value (T\({}_{\rm r}\)) for transforming an image from a grayscale into a binary image, in which each pixel has a value of 0 or 1, to distinguish the foreground and background of the image [28]. A pixel with an intensity greater (lower) than T\({}_{\rm r}\) is shown by a white-colored (black-colored) area. Threshold methods are grouped into two types: local thresholding and global thresholding. The local thresholding methods apply different threshold values to different regions of the image. Each T\({}_{\rm r}\) value is determined by the neighborhood of the pixel to which the threshold is applied [26,29]. On the other hand, in global thresholding, a single T\({}_{\rm r}\) value is used to separate the foreground and the background for all pixels in an image [24,30]. In the present analysis, we performed global thresholding with a common threshold T\({}_{\rm r}\) value of 128 as the second step of gamma-CT image segmentation. The black and white 2D segmented gamma-CT images of the scanned layers are shown in Figure 8, with the layers labeled from L\({}_{sg}\).3 to L\({}_{sg}\).20. The positions of the inserted rod turned entirely white, while the residual areas turned black. The image segmentation procedure resulted in a set of segments that collectively covered the whole image or a set of contours as the object's edge. Each pixel in the inserted rods' positions had a common property such as color or intensity. Furthermore, the neighboring areas differed substantially in terms of the same characteristic. When 2D segmented gamma-CT  
Figure 7: The arrangement of the inserted rods within the CT sample holder and the scanned layers in the \(z\) direction used for the 2D gamma-CT and NRF-CT images. images are created for the employment of the FV technique with the NRF-CT images after the gamma-CT image segmentation, the resulting contours can be used to precisely preserve the \({}^{208}\)Pb rod locations within the gamma-ray images of the CT sample and cut the surrounding noise and distortions.