# Determining Effective Threshold Range of Image Pixel Values for Municipal Waste-Contaminated Clay
## 1 Introduction  
The distribution, morphology, and particle size of dust, soils, and sediments can be identified using a Scanning Electron Microscope (SEM), so it is widely used in environmental studies [1; 2]. The microstructure of soil includes the particle morphology, particle arrangement, porosity, and contact relationship of particles [3; 4]. These variables not only determine the structural components and mutual connection strength of soil but also directly affect the soil engineering geological properties [5; 6; 7]. The structural deformation and strength change of soil under environmental change or external loading are the exterior manifestations of internal microstructural change [8; 9; 10; 11]. In addition, increasing attention has been given to the characteristics and structure of clay contaminated by municipal waste [12; 13; 14]. The microstructure and pixel threshold value of municipal waste-contaminated clay notably affect the assessment of its engineering characteristics. In many previous studies, the fractal dimension and apparent void ratio are commonly used parameters for analyzing the microstructure of soil [15; 16; 17; 18; 19]. The fractal dimension can be used to quantitatively describe the complexity of the particle shape profile, and the change in the apparent void ratio directly reflects the soil deformation [20; 21]. The structural properties of pores and particles in rock and soil can be quantitatively investigated with computer image processing software for segmenting and quantifying SEM images. As technology has developed, it has become commonplace to analyze the microstructure of rock and soil using computer image techniques [22; 23; 24]. To more correctly assess the microscopic properties of clay, many researchers have devised and developed image processing systems and codes, thereby achieving various research results. For example, [PERSON] et al. [25] developed a soil microstructure analysis system to quantify and analyze the microstructure characteristics of sand and clay, which involved three steps: pretreatment, identification and quantification of soil particles and pores, data output and storage; [PERSON] et al. [26] developed a code based on image analysis (MiCA), which could be used to quantitatively represent the direction and porosity of clay particles through SEM images; and [PERSON] et al. [27] developed image processing software Pores (Particles) and Cracks Analysis System (PCAS), which could be employed to accurately identify pores in SEM images and eliminate impurities by selecting the appropriate element radius and minimum area. The grayscale value and pore area of images vary for different threshold values because SEM image processing impacts the data extracted from images, while the void ratio derived from SEM image processing is highly influenced by the selected threshold [28]. Therefore, pixel threshold values are necessary for investigating the soil microstructure because the same soil exhibits distinct microstructure features under the application of the same research method but different threshold values. Currently, there are two main types of threshold determination methods: empirical and theoretical methods. In empirical methods, binarized images are visually compared with the original SEM images to determine the threshold value. These methods can be used to immediately determine the threshold, but they suffer the drawbacks of a time-consuming process and significant human error. Theoretical methods, which can be categorized as global or local methods, are based on certain threshold calculation theories [29], such as the Otsu algorithm [30; 31], iteration algorithm [32], image histograms [33], entropy criterion [34], gray wolf optimizer [35], and edge detection [36]. Although these theories could facilitate threshold calculation, they are not all appropriate for clay SEM image processing due to the very small size of soil particles and pores and the difficulty in identifying contours.