  Colorectal polyps are important precursors to colon cancer, a major health
problem. Colon capsule endoscopy (CCE) is a safe and minimally invasive
examination procedure, in which the images of the intestine are obtained via
digital cameras on board of a small capsule ingested by a patient. The video
sequence is then analyzed for the presence of polyps. We propose an algorithm
that relieves the labor of a human operator analyzing the frames in the video
sequence. The algorithm acts as a binary classifier, which labels the frame as
either containing polyps or not, based on the geometrical analysis and the
texture content of the frame. The geometrical analysis is based on a
segmentation of an image with the help of a mid-pass filter. The features
extracted by the segmentation procedure are classified according to an
assumption that the polyps are characterized as protrusions that are mostly
round in shape. Thus, we use a best fit ball radius as a decision parameter of
a binary classifier. We present a statistical study of the performance of our
approach on a data set containing over 18,900 frames from the endoscopic video
sequences of five adult patients. The algorithm demonstrates a solid
performance, achieving 47% sensitivity per frame and over 81% sensitivity per
polyp at a specificity level of 90%. On average, with a video sequence length
of 3747 frames, only 367 false positive frames need to be inspected by a human
operator.
