ABSTRACT
Capsule endoscopy (CE) has been widely applied in hospitals because it can be used to directly view the whole small intestine in the human body. However, a major drawback of this technology is the tedious review process of about 50,000 images produced in each examination. To relieve physicians and provide support for their decision making, computerized detection of disease is highly desired. In this chapter, we put forward a novel scheme for bowel polyp detection for CE images that integrates color and shape information, which are important visual clues for physicians. An illumination-invariant color feature built upon a chromaticity histogram is suggested. Combining it with Zernike moments that are scale, translation, and rotation invariant, we exploit the integrated information as color and shape features to discriminate polyp CE images from normal ones. By using a multilayer percetron neural network and support vector machine as classifiers, we perform experimental results on our collected CE data, illustrating encouraging performance of detection for polyp CE images.
