With 655,000 deaths worldwide per year, colorectal cancer it is the third most common form of cancer and the third leading cause of cancer-related death in the Western world. Colonoscopy is currently the preferred screening modality for prevention of colorectal cancer, in which a tiny camera is inserted into the colon to look for early signs of colorectal cancer. A recent systematic review calculated a 22% miss rate for all colonoscopic neoplasia, being 2.1% for advanced lesions. This could be attributed to factors such as inadequate endoscope withdrawal time, poor range of motion of the endoscope, and general endoscopist experience. Therefore the demand for quality control for colonoscopic procedures is increasing, and many researchers have been taking efforts in this area. In this paper, we first presented a novel technique - Colon Center Axis Determination Technique for Non-dark Lumen Images, and the performance evaluation result demonstrates that this technique enables a more accurate view mode classification for all kind of images. Secondly, we proposed two novel approaches to help objectively measure the quality of colonoscopy. A set of objective metrics has been proposed, and preliminary analysis result shows the spiral number during whole procedure/withdrawal phase has a relatively strong positive association with the ground truth circumferential inspection score. The other approach is using association rule mining knowledge to determine patterns of colon inspection. The preliminary result demonstrates that endoscopists with good and relatively poor inspection skill have different inspection patterns, and thus using patterns to assess colonoscopy quality would be anther feasible and promising method.