Informative Frame Automated Extraction from Colonoscopy Videos
Juan C. Arcila-Diaz, Victor Tuesta-Monteza, Heber I. Mejia-Cabrera,Maria P. Trujillo-Uribe, Kim Jeong-Hyun
Journal of Information Organization, Vol. 8, No. 1, 2018.
DOI: 
Link: http://www.dline.info/jio/fulltext/v8n1/jiov8n1_3.pdf
Abstract:
Colonoscopy videos contain blurred, non-informative frame sequences due to the rapid movements of the endoscope
during the exploration which need to be excluded to allow the expert physician to carry out his work in less time. In this
paper two methods of artificial vision are proposed for the automated extraction of informative frames based on detectable
characteristics of them. The first method allows for frame sorting into informative and non-informative based on the number of
contours detected in each frame. The second method makes use of the dense optical flux to determine the percentage of
individual frame motion, for group the frames whit K-Means algorithm by your motion in three groups: mean motion (informative
frame), large motion and little motion (non-informative frame). Both methods were successful in filtering out blurred
frames from colonoscopy video samples with the first method outperforming the second, i.e., 76.7% accuracy versus 74.7%,
respectively.