Ensemble of SVMs for incremental learning
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Real-time phase boundary detection for colonoscopy videos using motion vector templates
MICCAI'12 Proceedings of the 4th international conference on Abdominal Imaging: computational and clinical applications
SAPPHIRE: A toolkit for building efficient stream programs for medical video analysis
Computer Methods and Programs in Biomedicine
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Colonoscopy is the accepted screening method for detecting colorectal cancer or colorectal polyps. One of the main factors affecting the diagnostic accuracy of colonoscopy is the quality of bowel preparation. Despite a large body of published data on methods that could optimize cleansing, a substantial level of inadequate cleansing occurs in 10% to 75% of patients in randomized controlled trials. In this paper, we propose a novel approach that automatically determines percentages of stool areas in images of digitized colonoscopy video files, and automatically computes an estimate of the BBPS (Boston Bowel Preparation Scale) score based on the percentages of stool areas. It involves the classification of image pixels based on their color features using a new method of planes on RGB (Red, Green and Blue) color space. Our experiments show that the proposed stool classification method is sound and very suitable for colonoscopy video analysis where variation of color features is considerably high.