A data mining algorithmic approach for processing wireless capsule endoscopy data sets

Wireless capsule endoscopy (WCE) has been a breakthrough in recent medical technology. It is used to view the gastrointestinal tract and detect abnormalities such as bleeding, Crohn´s disease, peptic ulcers, and colon cancer. In this paper data mining techniques are utilized to extract useful information from a dataset of abnormal regions and non-abnormal regions. More specifically, the dataset contains polyps regions, ulcers regions and healthy regions. A number of features (shape descriptors, texture descriptors and color information) has been extracted for these regions and using a data mining toolbox useful conclusions are given on various relationships between these regions.