Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Automatic classification of digestive organs in wireless capsule endoscopy videos
Proceedings of the 2007 ACM symposium on Applied computing
Color and Position versus Texture Features for Endoscopic Polyp Detection
BMEI '08 Proceedings of the 2008 International Conference on BioMedical Engineering and Informatics - Volume 02
Colorectal Polyps Detection Using Texture Features and Support Vector Machine
MDA '08 Proceedings of the 3rd international conference on Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry
3D Reconstruction of Colon Segments from Colonoscopy Images
BIBE '09 Proceedings of the 2009 Ninth IEEE International Conference on Bioinformatics and Bioengineering
SAPPHIRE: A toolkit for building efficient stream programs for medical video analysis
Computer Methods and Programs in Biomedicine
Real-time advanced spinal surgery via visible patient model and augmented reality system
Computer Methods and Programs in Biomedicine
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Endoscopy is used for inspection of the inner surface of organs such as the colon. During endoscopic inspection of the colon or colonoscopy, a tiny video camera generates a video signal, which is displayed on a monitor for interpretation in real-time by physicians. In practice, these images are not typically captured, which may be attributed by lack of fully automated tools for capturing, analysis of important contents, and quick and easy retrieval of these contents. This paper presents the description and evaluation results of our novel software that uses new metrics based on image color and motion over time to automatically record all images of an individual endoscopic procedure into a single digitized video file. The software automatically discards out-patient video frames between different endoscopic procedures. We validated our software system on 2464h of live video (over 265 million frames) from endoscopy units where colonoscopy and upper endoscopy were performed. Our previous classification method achieved a frame-based sensitivity of 100.00%, but only a specificity of 89.22%. Our new method achieved a frame-based sensitivity and specificity of 99.90% and 99.97%, a significant improvement. Our system is robust for day-to-day use in medical practice.