Detection of quality visualization of appendiceal orifices using local edge cross-section profile features and near pause detection

Yi Wang, Wallapak Tavanapong, Johnny S. Wong, Junghwan Oh, Piet C. De Groen

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


Colonoscopy is an endoscopic technique that allows a physician to inspect the inside of the human colon. The appearance of the appendiceal orifice during colonoscopy indicates a complete traversal of the colon, which is an important quality indicator of the colon examination. In this paper, we present two new algorithms. The first algorithm determines whether an image shows the clearly seen appendiceal orifice. This algorithm uses our new local features based on geometric shape, illumination difference, and intensity changes along the norm direction (cross section) of an edge. The second algorithm determines whether the video is an appendix video (the video showing at least 3 s of the appendiceal orifice inspection). Such a video indicates good visualization of the appendiceal orifice. This algorithm utilizes frame intensity histograms to detect a near camera pause during the apendiceal orifice inspection. We tested our algorithms on 23 videos captured from two types of endoscopy procedures. The average sensitivity and specificity for the detection of appendiceal orifice images with the often seen crescent appendiceal orifice shape are 96.86% and 90.47%, respectively. The average accuracy for the detection of appendix videos is 91.30%.

Original languageEnglish (US)
Article number5290066
Pages (from-to)685-695
Number of pages11
JournalIEEE Transactions on Biomedical Engineering
Issue number3
StatePublished - Mar 2010
Externally publishedYes

Bibliographical note

Funding Information:
Manuscript received January 5, 2009; revised July 18, 2009 and September 4, 2009. First published October 20, 2009; current version published February 17, 2010. This work was supported in part by the U.S. National Science Foundation under Grant STTR-0740596, Agency for Healthcare Research and Quality, the Mayo Clinic, Grow Iowa Values Fund, and Iowa State University Research Foundation. The work of J. S. Wong, W. Tavanapong, and J. Oh was supported by EndoMetric, LLC, Ames, IA. Asterisk indicates corresponding author.


  • Appendiceal orifice detection
  • Appendix video detection
  • Colonoscopy
  • Edge cross section
  • Medical video analysis


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