TY - GEN
T1 - A framework for parsing colonoscopy videos for semantic units
AU - Cao, Yu
AU - Tavanapong, Wallapak
AU - Kim, Kihwan
AU - Wong, Johnny
AU - Oh, Jung Hwan
AU - De Groen, Piet C.
PY - 2004
Y1 - 2004
N2 - Colonoscopy is an important screening procedure for colorectal cancer. During this procedure, the endoscopist visually inspects the colon. Currently, there is no content-based analysis and retrieval system that automatically analyzes videos captured from colonoscopic procedures and provides a user-friendly and efficient access to important content. Such a system will be valuable for endoscopic research and education. The first necessary step for the analysis is parsing for semantic units. Since the characteristics of colonoscopy videos differ from those of videos studied in the literature, we introduce a new video parsing framework that includes (i) a new scene definition and a new video parsing paradigm and (ii) a novel scene segmentation algorithm using audio analysis and finite state automata to recognize scenes and associated boundaries. Our experimental results show average precision and recall of 95% and 81 % for parsing scenes, respectively. The framework is extensible to videos captured from other endoscopic procedures such as upper gastrointestinal endoscopy, enteroscopy, cystoscopy, and laparoscopy.
AB - Colonoscopy is an important screening procedure for colorectal cancer. During this procedure, the endoscopist visually inspects the colon. Currently, there is no content-based analysis and retrieval system that automatically analyzes videos captured from colonoscopic procedures and provides a user-friendly and efficient access to important content. Such a system will be valuable for endoscopic research and education. The first necessary step for the analysis is parsing for semantic units. Since the characteristics of colonoscopy videos differ from those of videos studied in the literature, we introduce a new video parsing framework that includes (i) a new scene definition and a new video parsing paradigm and (ii) a novel scene segmentation algorithm using audio analysis and finite state automata to recognize scenes and associated boundaries. Our experimental results show average precision and recall of 95% and 81 % for parsing scenes, respectively. The framework is extensible to videos captured from other endoscopic procedures such as upper gastrointestinal endoscopy, enteroscopy, cystoscopy, and laparoscopy.
KW - Content-based analysis
KW - Medical image processing
KW - Scene segmentation
UR - http://www.scopus.com/inward/record.url?scp=11244281757&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=11244281757&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:11244281757
SN - 0780386035
SN - 9780780386037
T3 - 2004 IEEE International Conference on Multimedia and Expo (ICME)
SP - 1879
EP - 1882
BT - 2004 IEEE International Conference on Multimedia and Expo (ICME)
T2 - 2004 IEEE International Conference on Multimedia and Expo (ICME)
Y2 - 27 June 2004 through 30 June 2004
ER -