Paper Info
Title
Detection of quality visualization of appendiceal orifices using local edge cross-section profile features and near pause detection.
Abstract
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%.
Year
DOI
Venue
2010
10.1109/TBME.2009.2034466
IEEE Trans. Biomed. Engineering
Keywords
Field
DocType
video signal processing,edge cross section,medical signal detection,endoscopes,near pause detection,biomedical optical imaging,illumination difference,near camera pause,endoscopic technique,geometric shape,human colon,feature extraction,appendiceal orifices,appendiceal orifice detection,quality visualization detection,appendix video detection,appendix video,medical video analysis,local edge cross-section profile features,intensity changes,colonoscopy,inspection,cross section,visualization,algorithms,lighting,histograms,orifices,shape
Video recording,Histogram,Computer vision,Visualization,Computer science,Image quality,Feature extraction,Body orifice,Geometric shape,Artificial intelligence
Journal
Volume
Issue
ISSN
57
3
1558-2531
Citations 
PageRank 
References 
11
0.81
12
Authors
5
Name
Order
Citations
PageRank
Yi Wang11520135.81
Wallapak Tavanapong2865.71
Johnny Wong350049.19
JungHwan Oh452044.87
Piet C. De Groen537229.89