Computer-aided detection of bleeding regions for capsule endoscopy images.

Capsule endoscopy (CE) has been widely used to diagnose diseases in human digestive tract. However, a tough problem of this new technology is that too many images to be inspected by eyes cause a huge burden to physicians, so it is significant to investigate computerized diagnosis methods. In this paper, a new computer-aided system aimed for bleeding region detection in CE images is proposed. This new system exploits color texture feature, an important clue used by physicians, to analyze status of gastrointestinal tract. We put forward a new idea of chrominance moment as the color part of color texture feature, which makes full use of Tchebichef polynomials and illumination invariant of hue/saturation/intensity color space. Combined with uniform local binary pattern, a current texture representation model, it can be applied to discriminate normal regions and bleeding regions in CE images. Classification of bleeding regions using multilayer perceptron neural network is then deployed to verify performance of the proposed color texture features. Experimental results on our bleeding image data show that the proposed scheme is promising in detecting bleeding regions.