Gastric cancer is the world's most common cause of cancer-related deaths, which has changed in recent years. To order to reduce mortality, endoscopic stomach biopsy is commonly performed for early detection of gastric cancer. Image segmentation is a crucial technique for interpreting and intensifying the medical image. The purpose of this study was to create a visual feature segmentation impact of gastric cancer system to determine the risk of gastric cancer development achieved through image processing on chromoendoscopy images. This system was developed on the basis of the improved simple Linear Iterative Clustering (ISLIC) and Statistical Region Merging (SRM) algorithm. This approach can allow an effective selection of the high-risk population of gastric cancer that needs endoscopy follow-up. The conducted CAD (Computer Aided Diagnosis) system works as an assistant to gastroenterology physicians, assisting in defining the cancer region in the scaffold's endoscopic images taking biopsies from these areas and making a better diagnosis.
Volume 11 | 12-Special Issue