In this paper we present a novel method to detect abnormal regions from capsule endoscopy images. Wireless Capsule
Endoscopy (WCE) is a recent technology where a capsule with an embedded camera is swallowed by the patient to
visualize the gastrointestinal tract. One challenge is one procedure of diagnosis will send out over 50,000 images,
making physicians' reviewing process expensive. Physicians' reviewing process involves in identifying images
containing abnormal regions (tumor, bleeding, etc) from this large number of image sequence. In this paper we construct
a novel framework for robust and real-time abnormal region detection from large amount of capsule endoscopy images.
The detected potential abnormal regions can be labeled out automatically to let physicians review further, therefore,
reduce the overall reviewing process. In this paper we construct an abnormal region detection framework with the
following advantages: 1) Trainable. Users can define and label any type of abnormal region they want to find; The
abnormal regions, such as tumor, bleeding, etc., can be pre-defined and labeled using the graphical user interface tool we
provided. 2) Efficient. Due to the large number of image data, the detection speed is very important. Our system can
detect very efficiently at different scales due to the integral image features we used; 3) Robust. After feature selection
we use a cascade of classifiers to further enforce the detection accuracy.