Open Access
28 April 2015 Automated frame selection process for high-resolution microendoscopy
Ayumu Ishijima, Richard A. Schwarz, Dongsuk Shin, Sharon Mondrik, Nadarajah Vigneswaran, Ann M. Gillenwater M.D., Sharmila Anandasabapathy, Rebecca Richards-Kortum
Author Affiliations +
Abstract
We developed an automated frame selection algorithm for high-resolution microendoscopy video sequences. The algorithm rapidly selects a representative frame with minimal motion artifact from a short video sequence, enabling fully automated image analysis at the point-of-care. The algorithm was evaluated by quantitative comparison of diagnostically relevant image features and diagnostic classification results obtained using automated frame selection versus manual frame selection. A data set consisting of video sequences collected in vivo from 100 oral sites and 167 esophageal sites was used in the analysis. The area under the receiver operating characteristic curve was 0.78 (automated selection) versus 0.82 (manual selection) for oral sites, and 0.93 (automated selection) versus 0.92 (manual selection) for esophageal sites. The implementation of fully automated high-resolution microendoscopy at the point-of-care has the potential to reduce the number of biopsies needed for accurate diagnosis of precancer and cancer in low-resource settings where there may be limited infrastructure and personnel for standard histologic analysis.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Ayumu Ishijima, Richard A. Schwarz, Dongsuk Shin, Sharon Mondrik, Nadarajah Vigneswaran, Ann M. Gillenwater M.D., Sharmila Anandasabapathy, and Rebecca Richards-Kortum "Automated frame selection process for high-resolution microendoscopy," Journal of Biomedical Optics 20(4), 046014 (28 April 2015). https://doi.org/10.1117/1.JBO.20.4.046014
Published: 28 April 2015
Lens.org Logo
CITATIONS
Cited by 18 scholarly publications and 2 patents.
Advertisement
Advertisement
KEYWORDS
Video

Cancer

Algorithm development

Image analysis

Point-of-care devices

Image classification

Image quality

Back to Top