## 3 Micro-CT Image and Characterization of the Pore Structure  
### SEM and Micro-CT Imaging Method  
The morphology of foamed samples can be investigated with different descriptive approaches. For example, secondary electron (SE) images of the foamed concrete specimens in Figure 2, which are performed by SEM, can be used. In these images, numerous spherical pores with different sizes in each specimen can be identified. Moreover, as shown in Figure 3, typical components of cement-based materials, such as C-S-H (calcium silicate hydrates), portlandite (calcium hydroxide) and ettringite, can be identified using the SEM images at acceptable magnification. Focusing on the cement paste, it is clear that the hydration degrees of the specimens are in a relatively early state, and the hardness \begin{table}
\begin{tabular}{c c c c c} \hline \hline Specim & LithoPore®100 & LithoPore®200 & LithoPore®450 & LithoPore®600 \\ \hline Target (dry) density (kg/m3) & 100 & 200 & 450 & 600 \\ Wet density (kg/m3) & 150–158 & 305–315 & 530–545 & 710–760 \\ \hline Cement content (kg/m3) & 61 & 170 & 215 & 285 \\ Water (kg/m3) & 28 & 77 & 125 & 225 \\ Foam content (kg/m3) & 67.2 & 64.3 & 59.2 & 52.0 \\ Filler (fly) ash and calcium carbonate (kg/m3) & - & - & 95 & 215 \\ Network former LithoPcan® (kg/m3) & 6 & 5 & 5 & 4 \\ Stabilizer LithoPcan® (kg/m3) & 0.6 & 0.6 & 0.6 & 0.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mix design for foam concrete specimens. Figure 1: Foamed concrete specimens with different densities. will further increase with increasing hydration time. Comparing the amount of C-S-H contents in the lower density samples with those in the higher density samples in Figure 3, the hydration process is more advanced at higher density samples, and the phase composition influences the material strength; this indicates that the thickness of the solid wall, as well as the degree of hydration can affect the strength and thermal property of materials with increasing densities. However, SEM images can only provide the pore characteristics on the surface of the specimen, and the specimens can be easily damaged during the preparation process. In this study, for more detailed examination of the pore characteristics inside the samples, micro-CT images are utilized to examine the pore sizes and distributions of the foamed concrete non-destructively and noninvasively. Figure 4 shows the process of the micro-CT imaging. The first and second figures in Figure 4 are the original eight-bit and region of interest (ROI) of the micro-CT image, respectively. To classify the pores inside the foamed specimen, the cross-sectional binary images are generated by using a proper threshold. Before binarizing the original image, the Bronnikov algorithm [PERSON] (1970) is used to improve the micro-CT image contrast. The threshold here is selected based on the Otsu method Otsu (1971) and experiments. In the original image, a set of pixels that have lower values than the threshold are considered as pores. The binary image is composed of 1000 \(\times\) 1000 pixels. In the binary image in Figure 4, the white parts represent the voids, and the black parts represent the solid part (cell wall) of the foamed specimen. The 3D binary model of the foamed sample in Figure 4 is obtained by stacking 1000 binarized images. Figure 3: SEM images of LithoPore®100 (density = 100 kg/m\({}^{3}\)) and LithoPore®450 specimens (density = 450 kg/m\({}^{3}\)) at 5000\(\times\) magnification. Figure 2: SEM images of the foamed concrete specimens with different densities. In addition to the binary imaging, an image segmentation is conducted to enhance the micro-CT image segmentation quality. In Figure 4, some solid parts of the foamed specimen are neglected or faintly described due to the limitation of image resolution. To overcome this phenomenon, a watershed segmentation process is adopted here. The watershed segmentation is an imaging method to visualize a gray level image into its topographic representation [31; 32; 33].