AdaBoost-Based Cecum Recognition System in Accordance with Boston Bowel Preparation Scale

In this thesis, we proposed a system which can automatically recognize the cecum image from colonoscopy photos based on the variability of human intestinal. This system can assist doctors to check the colonoscopy photos and reduce the load on doctors. In recent years, the colorectal cancer is the top one cancer on incidence rate and medical expenses in Taiwan. Fortunately, early treatment of colorectal cancer in Tis and T1 can increase the survival rate of patient effectively. However, there is no symptom in the early stage of colorectal cancer. In order to detect the early stage of colorectal cancer, the colonoscopy examination regularly is very important. The colonoscopy quality is closely related to the detection of early cancer. There are some quality indicators for colonoscopy: Cecal Intubation Rate (CIR), Bowel Preparation (BP), Adenoma Detection Rate (ADR), and Withdrawal Time (WT). In this thesis, we focus on CIR and BP. In order to evaluate CIR, doctors need to view great amount of colonoscopy photos. Therefore we propose a cecum recognition system to help doctors to evaluate CIR automatically. The system will assess BP if so bad that we cannot get information and features in the image. Then, the system extracts features of cecum from the images with good BP by image processing, and we use machine learning algorithm to recognize cecum images. Our method achieves the average accuracy rate of 94.0% and the best accuracy rate of 96.9%.