{'fraction_non_alphanumeric': 0.019592476489028215, 'fraction_numerical': 0.004702194357366771, 'mean_word_length': 4.726457399103139, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
AUTOMATIC DETECTION OF COLORECTAL POLYPS IN STATIC IMAGES

Colorectal cancer continues to be one of the leading causes of mortality worldwide. Scanning using colorectal endoscopy is a useful and common method in clinical examinations. However, the scanning and polyps detections are performed by physicians. Failures to detect polyps might be caused due to lack of experience or knowledge. The purpose of this paper is to discover a scheme able to distinguish polyps from normal tissue in static images off-line.Texture features are studied for the discrimination between polyps and normal tissue. Two useful and simple features are proposed. The student's t-test is applied in selecting useful features to reduce the computation time. The support vector machine is used as a classifier to identify the position of polyps. A study on the numbers in the training patterns is done in order to select an optimal ratio between the polyps and non-polyps sub-images. Seventy-four colonoscopic images are collected to test the system. Half are used as training images and half for testing. The experimental result shows the system can identify all polyps if the colonoscopic images contain a single polyp. The sensitivity is 86.2% and the false-positive rate is 1.26 marks per image.