Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Algorithms
Automatic extraction of motion trajectories in compressed sports videos
Proceedings of the 12th annual ACM international conference on Multimedia
A unified approach to shot change detection and camera motion characterization
IEEE Transactions on Circuits and Systems for Video Technology
Rapid estimation of camera motion from compressed video with application to video annotation
IEEE Transactions on Circuits and Systems for Video Technology
Mining colonoscopy videos to measure quality of colonoscopic procedures
BIEN '07 Proceedings of the fifth IASTED International Conference: biomedical engineering
Arthemis: Annotation software in an integrated capturing and analysis system for colonoscopy
Computer Methods and Programs in Biomedicine
Deadline-constrained media uploading systems
Multimedia Tools and Applications
Improving the quality of color colonoscopy videos
Journal on Image and Video Processing - Color in Image and Video Processing
Automatic Labeling of Colonoscopy Video for Cancer Detection
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
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Colonoscopy is the accepted screening method for detection of colorectal cancer or its precursor lesions, colorectal polyps. Indeed, colonoscopy has contributed to a decline in the number of colorectal cancer related deaths. However, not all cancers or large polyps are detected at the time of colonoscopy, and methods to investigate why this occurs are needed. We present a new computer-based method that allows automated measurement of a number of metrics that likely reflect the quality of the colonoscopic procedure. The method is based on analysis of a digitized video file created during colonoscopy, and produces information regarding insertion time, withdrawal time, images at the time of maximal intubation, the time and ratio of clear versus blurred or non-informative images, and a first estimate of effort performed by the endoscopist. As these metrics can be obtained automatically, our method allows future quality control in the day-to-day medical practice setting on a large scale. In addition, our method can be adapted to other healthcare procedures. Last but not least, our method may be useful to assess progress during colonoscopy training, or as part of endoscopic skills assessment evaluations.