CT Image Analysis of Gastrointestinal Tumors Based on Simple Linear Iterative Cluster Algorithm

Abstract

Objective: Gastrointestinal cancer is a very common disease at present. The purpose of this experiment is to use CT scanning technology and Simple Linear Iterative Cluster (SLIC) algorithm to analyze and fuse the scanning results, to explore the imaging characteristics of gastrointestinal neuroendocrine tumors by CT scanning and its application value in gastrointestinal tumors. Methods: The medical records of 25 patients with gastrointestinal tumors were selected as samples and analyzed retrospectively. Texture information fusion of CT image based on SLIC algorithm. This algorithm can fuse the texture information in the image, and then propose more targeted treatment for gastrointestinal tumors in different periods. Results: It was found that the diameter of malignant gastrointestinal tumors was more than 5 cm, and most of them occurred in the intestinal tract and the edge. The specific manifestations are blurred or lobulated, uneven density, invasion of surrounding structures and combined transfer rate. Therefore, it can be concluded that there are significant differences in CT features between benign and malignant gastrointestinal tumors. CT examination is helpful to differentiate benign and malignant tumors. Conclusion: Based on CT imaging, it can be found that the main cause of gastric neurosecretory tumors is blood-rich lesions. Gastrointestinal cancer cells of different pathological grades have different CT imaging features. CT imaging has certain value for preliminary judgment of pathological grading of patients.