Intelligent Image Feature Matching for Small Intestine Capsule Endoscopy

DESCRIPTION (provided by applicant): Diseases of the small intestine affect nearly 19 million Americans. The care of these patients has improved significantly via the recent introduction of "capsule endoscopy". This involves swallowing a pill-shaped imaging device which wirelessly transmits images to an external receiver. For the first time, this has enabled non-invasive visual imaging of the small intestinal mucosa. The technology has the potential to provide a diagnosis to the 30 - 50 % of patients whose occult gastrointestinal bleeding remains unexplained even after thorough workup with esophagogastroduodenoscopy (EGD) and colonoscopy. However, capsule endoscopy involves manual review of approximately 50,000 images taken by the device. Furthermore, localization of a particular lesion seen in the images is challenging due to the unknown motility of the capsule. This prompted our group to investigate the use of visual motion tracking to enhance review of capsule endoscopy studies. We propose to create a single reconstructed image representation of the small intestine from the individual capsule images. This will provide three key advantages: 1) Images will be combined to eliminate redundant review of the same area of the intestinal lumen, thereby decreasing total review time. 2) By creating spatial relations among images, lesion localization will be possible; and 3) If images are non-overlapping, we will be able to identify unimaged areas, which current capsule endoscopy systems cannot do. Our Phase I effort involves development of the intelligent image feature matching software and evaluation using images from a capsule endoscopy platform. We will evaluate 1) feature identification, 2) feature matching between images, and 3) the ability to measure intraluminal distances based on the captured images of the capsule endoscope. If successful, we envision a Phase II effort that will harness our intelligent feature matching technology to construct an advanced GI image browsing system. We propose to create a single mosaiced representation of the small bowel from the individual capsule images. The system will be evaluated in a pilot human study to investigate its ability to quickly visualize and localize potential lesions. It is our long term goal to provide gastroenterologists with a non-invasive tool for visualization of the small intestine which is quick to interpret and allows accurate localization of lesions. Capsule endoscopy for the first time has enabled non-invasive visual imaging of the distal small intestinal mucosa. However, it is hindered by time-consuming manual reviews and an inability to localize lesions. Our proposed software technology will provide gastroenterologists with a quick and efficient tool to review capsule endoscopy data.