## 4 Discussion  
At the beginning of this study, we collected 298 patients' images and data in one-and-a-half years. The main purpose of this was to analyze the analysis mode of the larger quantity of data subsequently collected by analyzing smaller data quantities. After the preliminary image analysis, all image analysis data were quantified into an MS Excel table, and some texture features were demonstrated to exhibit Pearson correlation to CEAs. We observed that Pearson correlation between luminosity, chromaticity, and texture characteristics of the patient's colon mucosa did not differ significantly after correlation by patient's normal anal skin image data. The method of correcting the color of the patient's anal skin is not used. We used Image J software to cut images and to minimize interference from feces and reflections. In addition, the study calculated that the ADR was 23.6% in the Division of Gastroenterology and Hepatology, Department of Internal Medicine, Taipei Medical University Hospital. The ADR of patients with MC was also calculated; it was higher in patients with MC higher in other patients, and the ratio of colorectal cancer was similar to the average rate in the general population. Next, 1092 patient images were analyzed. Using the database to collect six years' worth of images and data, 543 patient images were ultimately enrolled for analysis. The correlation for GLCM analysis and CEAs has not improved, but we noted that patients with MC who had polyps also had five GLCM texture features correlated with pathological results. The three correlations were autocorrelation, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Correlation Number = 181** & **Contrast** & **CorrelationM** & **Correlation** & **Difference\_Variance** \\ \hline \multirow{2}{*}{Polyps pathological grade (1–3)} & Pearson correlation & 0.266 & 0.235 & 0.235 & 0.266 \\ \cline{2-6}  & Two-tailed significance & \(p<0.001\) & \(p=0.001\) & \(p=0.001\) & \(p<0.001\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of GLCMs and other pathological results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Correlation Number = 181** & **Autocorrelation** & **Cluster\_Prominence** & **Cluster\_Shade** \\ \hline \multirow{2}{*}{Polyp pathological grade (1–3)} & Pearson correlation & 0.390 & 0.398 & 0.396 \\ \cline{2-5}  & Two-tailed significance & \(p<0.001\) & \(p<0.001\) & \(p<0.001\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: All GLCM features and pathology correlation. cluster_prominence, and cluster_shade; other features such as contrast, correlationM, correlation, and difference_variance also possess a certain degree of relevance. The five aforementioned texture features should be applicable to clinical colonoscopy image analysis. No evidence currently suggests that MC is directly related to malignancies. But MC is related to adenoma [1] and adenoma is related to colorectal cancer [27]. To establish whether tumor markers (such as CEAs, CA199, CA125, AFP, or CA724) are related to texture features after calculation may require more data and image analysis studies. MC images require much clinical analysis, and the results should be provided for gastroenterologists prescribing relevant laxatives, mainly because MC is a reversible condition. For instance, if a patient who has MC with polyps exhibits particular textures or colon mucosal changes under colonoscopy, doctors can terminate the use of the relevant medication and prescribe different laxatives instead. In addition, contrast mode reflects the clarity of the image and the degree of texture groove depth. The previous study did not mention that longer use of a relevant drug caused darker contrast. More data collection may confirm more correlations. Therefore, the longer drugs potentially able to affect colon mucosa coloring are used, the more obvious the texture. The effects of intestinal bacteria can be further analyzed in the future.