## Materials and Methods
### Image Texture Feature Analysis  
Image texture features may be analyzed in many ways, with common methods including GLCMs, gray-level co-occurrence histograms, gray-level run length matrices, gray-level size zone matrices, neighboring gray tone difference matrices, gray-level dependence matrices, and etc. Although many new technologies have been developed so far, some traditional feature analysis methods still have their value. GLCMs are mainly used to count the probability of occurrence of pixel pairs in different directions and distances and our study focuses on similar textures on polyps and MC mucosa, so we selected GLCM method for this study. The GLCM represents the grayscale value change of the relative positions for statistical pixels in space, reflecting the distribution of texture in space. The state was developed by [PERSON] et al. in 1973 [PERSON] (1973). [PERSON] believed that graphic texture is composed of multiple texture units and that the texture is caused by the repeated occurrence of the grayscale distribution in a spatial position. Therefore, a certain grayscale occurs between two pixels separated by a certain length in the image space. The spatial relationship of grayscale in an image can exhibit graphical characteristics [PERSON] (1973). The GLCM of an image can reflect information about the gray level of the image, such as that with respect to direction, adjacent interval, and variation amplitude. It constitutes the basis for analyzing the local patterns of images and their arrangement rules. In simple terms, data may be recorded in N\(\times\)N square matrices according to grayscale relationships of pixels in images. For example, if the first lattice in a matrix is 1, only one pair of grayscales is 1 pixel horizontally adjacent. It is illustrated in Figure 3.  
The GLCM has been widely used for texture analysis of various images types. Following various amendments, feature analysis methods were updated to improve results [PERSON] (1973). Ultrasound analysis for breast tumors [PERSON] (1973) and rotator cuff tears [PERSON] (1974), computed tomography for liver tumors [PERSON] (1976) and hyperacute stroke [PERSON] (1976), and image analysis for retinal vessels [PERSON] (1977) have been successfully performed. The use  
Figure 3: The gray-level co-occurrence matrices (GLCMs) features were extracted from four directions (\(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\), and \(135^{\circ}\)) with distance of 1 pixel from the centered pixel (\(\bullet\)). of computed tomography to simulate colonoscopy for polyp detection has also been studied [24]. In addition, one study analyzed pathological biopsies for colorectal cancer and normal colorectal mucosa [25], and this technique can provide clinicians with valuable assistance. Because this represents the earliest and the most mature image texture analysis method, this study analyzes the colorectal polyp image texture characteristics of patients with MC. The GLCM has a total of 14 features: autocorrelation, dissimilarity, energy, entropy, homogeneity, difference variance, difference entropy, information measure of correlation, inverse difference normalized, inverse difference moment, cluster prominence, cluster shade, contrast, and correlation. These features can be clinically applied to the analysis of various images. For the analysis of medical images, the features are employed mainly to study their correlations with various lesions. Of the 14 features analyzed in this study, only eight were related to the final results: entropy, energy, correlation, dissimilarity, homogeneity, autocorrelation, cluster_prominence, and cluster_shade. Entropy is a measure of the quantity of information that an image possesses. Texture information also qualifies as image information. It is a measure of randomness. When all elements in the co-occurrence matrix possess maximum randomness and all values in the spatial co-occurrence matrix are almost equal, the elements in the co-occurrence matrix are dispersed, and the entropy (fluctuation) is large.