  We present a computer-aided detection algorithm for polyps in optical
colonoscopy images. Polyps are the precursors to colon cancer. In the US alone,
more than 14 million optical colonoscopies are performed every year, mostly to
screen for polyps. Optical colonoscopy has been shown to have an approximately
25% polyp miss rate due to the convoluted folds and bends present in the colon.
In this work, we present an automatic detection algorithm to detect these
polyps in the optical colonoscopy images. We use a machine learning algorithm
to infer a depth map for a given optical colonoscopy image and then use a
detailed pre-built polyp profile to detect and delineate the boundaries of
polyps in this given image. We have achieved the best recall of 84.0% and the
best specificity value of 83.4%.
