Non-negative Matrix Factorization for Endoscopic Video Summarization
SETN '08 Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications
Detecting Informative Frames from Wireless Capsule Endoscopic Video Using Color and Texture Features
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Computers in Biology and Medicine
Sudden Changes Detection in WCE Video
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
A semi-supervised learning method for motility disease diagnostic
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Using ensemble classifier for small bowel ulcer detection in wireless capsule endoscopy images
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
An intelligent system to detect Crohn's disease inflammation in wireless capsule endoscopy videos
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Automatic region-of-interest segmentation and pathology detection in magnetically guided
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Reducing redundancy in wireless capsule endoscopy videos
Computers in Biology and Medicine
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Wireless capsule video endoscopy is a novel and challenging clinical technique, whose major reported drawback relates to the high amount of time needed for video visualization. In this paper, we propose a method for the rejection of the parts of the video resulting not valid for analysis by means of automatic detection of intestinal juices. We applied Gabor filters for the characterization of the bubble-like shape of intestinal juices in fasting patients. Our method achieves a significant reduction in visualization time, with no relevant loss of valid frames. The proposed approach is easily extensible to other image analysis scenarios where the described pattern of bubbles can be found.