Wireless capsule endoscopy (WCE) has been intensively researched recently due to its convenience for diagnosis and
extended detection coverage of some diseases. Typically, a full recording covering entire human digestive system
requires about 8 to 12 hours for a patient carrying a capsule endoscope and a portable image receiver/recorder unit,
which produces 120,000 image frames on average. In spite of the benefits of close examination, WCE based test has a
barrier for quick diagnosis such that a trained diagnostician must examine a huge amount of images for close
investigation, normally over 2 hours. The main purpose of our work is to present a novel machine vision approach to
reduce diagnosis time by automatically detecting duplicated recordings caused by backward camera movement, typically
containing redundant information, in small intestine. The developed technique could be integrated with a visualization
tool which supports intelligent inspection method, such as automatic play speed control. Our experimental result shows
high accuracy of the technique by detecting 989 duplicate image frames out of 10,000, equivalently to 9.9% data
reduction, in a WCE video from a real human subject. With some selected parameters, we achieved the correct detection
ratio of 92.85% and the false detection ratio of 13.57%.
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