# State-of-the-Art Research on Loess Microstructure Based on X-ray Computer Tomography
## 2 The Development History of CT Technology
### CT Image Processing and Analysis  
The core components of the image processing sequence are noise suppression, contour intensification, image partitioning, subsequent treatment, and structural evaluation [79]. Within X-ray CT imaging, the most challenging and extensively studied aspect is typically rooted in thresholding and segmentation procedures. The precision of soil porosity calculations from X-ray CT analysis is intrinsically linked to the thresholding process, which is primarily due to the correlation between the computed soil porosity and the grayscale values. [PERSON] et al. [80] identified that errors associated with the thresholding step can range from 15% to 40% when calculating the visible total soil porosity and the saturated hydraulic conductivity. Prior to analysis, images must undergo thresholding based on diverse histogram shapes, spatial clustering, entropy measures, object attributes, spatial correlations, and local gray-level surface information [81]. These six categories facilitate the differentiation between soil particles and pores, converting the grayscale image into a binary format that smooths the boundaries between particles and pores [82]. Throughout this process, a variety of statistical and mathematical techniques can be implemented to enhance the accuracy and quality of the segmentation. Currently, threshold-based segmentation techniques, encompassing both local and global approaches [79; 83], are extensively utilized in image analysis. These methods discretize images into distinct datasets, enabling the differentiation between solid phases and soil pores, which is essential for the quantitative characterization of porous media. Local segmentation methods, in particular, demonstrate superior performance and robustness when addressing local image heterogeneity, making them the foundation of most advanced segmentation techniques [84]. [PERSON] et al. [65] evaluated four commonly employed threshold-based segmentation methods in soil research, including thresholding techniques, adaptive window indicator kriging, fully automatic extension methods, and global segmentation. While the first three methods offer consistent image processing capabilities, they differ in their sensitivity to noise, with the initial two methods often leading to the underrepresentation of smaller pores and the smoothing of larger pore surfaces [85]. [PERSON] et al. [86] highlighted that the soil's porous framework is rarely captured accurately with a single threshold, underscoring the importance of selecting an appropriate threshold to secure the reliability of the segmentation for each CT photograph. Other segmentation methods in use include region growth [87] (e.g., [PERSON] et al. [88] utilized micro-focus X-ray CT combined with digital image correlation (DIC) to analyze the strain localization behavior of partially saturated sand under triaxial compression. The study unveiled the macroscopic and microscopic progression of shear bands, as well as the alterations in the granular structure. A tri-value thresholding technique based on the region-growing method was proposed, which successfully differentiated between the solid phase, water phase, and gas phase. The calculated local porosity ratio and degree of saturation were found to be in good agreement with the measured values. ), watershed algorithms [89], and clustering-based unsupervised and supervised learning approaches [90]. Among these, supervised machine learning methods, such as neural networks, random forests, support vector machines, linear and logistic regression, and classification trees, are widely adopted for their ability to learn from well-labeled data and effectively predict outcomes for new data or images [91]. [PERSON] et al. [92] conducted a review of various software tools and methods for evaluating pore size distribution (PSD) using X-ray CT images. They assessed the performance of seven major image analysis techniques (Avizo, CT Analyser, BoneJ, Quantim4, DTM, DFS-FIJI, and 3 DMA) on the CT images of different soil types.