A comparative study of endoscopic polyp detection by textural features

Digestive tract cancer is a big threat to human and capsule endoscopy (CE) is a relatively new technology to detect the diseases in the small bowel. Since polyp is an important symptom of digestive cancer it is important to detect them by computerized methods. In this work, we comparatively investigate computer aided detection for polyps by machine learning based methods that are built upon color textural features. Four textural features, wavelet based features, color wavelet covariance, rotation invariant uniform local binary pattern and complete local binary pattern, are utilized to characterize the textural features in CE images, and performance of them are extensively studied in three different color spaces, that is, RGB, HSI and Lab color spaces.