Automatic identification of colonic polyp in high-resolution CT Images

Automatic polyp detection is a challenging task as polyps come in different sizes and shape. The detection generally consists of colon segmentation, identification of suspected polyps and classification. Classification involves discriminating polyps from among many suspected regions based on a number of features extracted from the detected regions. This paper presents the work on the first two stages of the detection. For the colon segmentation, the fuzzy connectivity region growing technique is used while for the identification of suspected polyps concave region searching is applied. A rule-based filtering based on 3D volumetric features is used to reduce a large number of non-polyp structures (false positives). The method is fast, robust and validated with a number of high-resolution colon datasets.