Basic Study
Copyright ©The Author(s) 2019. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Mar 14, 2019; 25(10): 1197-1209
Published online Mar 14, 2019. doi: 10.3748/wjg.v25.i10.1197
Quest for the best endoscopic imaging modality for computer-assisted colonic polyp staging
Georg Wimmer, Michael Gadermayr, Gernot Wolkersdörfer, Roland Kwitt, Toru Tamaki, Jens Tischendorf, Michael Häfner, Shigeto Yoshida, Shinji Tanaka, Dorit Merhof, Andreas Uhl
Georg Wimmer, Roland Kwitt, Andreas Uhl, Department of Computer Sciences, University of Salzburg, Salzburg 5020, Austria
Michael Gadermayr, Dorit Merhof, Interdisciplinary Imaging and Vision Institute Aachen, RWTH Aachen, Aachen 52074, Germany
Gernot Wolkersdörfer, Department of Internal Medicine I, Paracelsus Medical University/Salzburger Landeskliniken (SALK), Salzburg 5020, Austria
Toru Tamaki, Department of Information Engineering, Graduate School of Engineering, Hiroshima University, Hiroshima 7398527, Japan
Jens Tischendorf, Internal Medicine and Gastroenterology, University Hospital Aachen, Würselen 52146, Germany
Michael Häfner, Department of Gastroenterologie and Hepatologie, Krankenhaus St. Elisabeth, Wien 1080, Austria
Shigeto Yoshida, Department of Endoscopy and Medicine, Graduate School of Biomedical and Health Science, Hiroshima University, Hiroshima 7348551, Japan
Shinji Tanaka, Department of Endoscopy, Hiroshima University Hospital, Hiroshima 7348551, Japan
Author contributions: Wimmer G and Gadermayr M performed the experiments; Wimmer G, Gadermayr M, Merhof D and Uhl A coordinated the research; Tamaki T, Tischendorf J, Häfner M, Yoshida S and Tanaka S provided the endoscopic image databases; Wimmer G, Gadermayr M, Wolkersdörfer G and Uhl A wrote the paper.
Supported by the Austrian Science Fund (FWF), KLI project 429, No. TRP206.
Conflict-of-interest statement: There are no conflicts of interest.
Data sharing statement: No additional data are available.
ARRIVE guidelines statement: The authors have read the ARRIVE guidelines, and the manuscript was prepared and revised according to the ARRIVE guidelines.
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
Corresponding author: Georg Wimmer, PhD, Postdoc, Department of Computer Sciences, University of Salzburg, Jakob Haringer Strasse 2, Salzburg 5020, Austria.
Telephone: +43-662-80446035
Received: December 14, 2018
Peer-review started: December 14, 2018
First decision: January 18, 2019
Revised: February 13, 2019
Accepted: February 15, 2019
Article in press: February 16, 2019
Published online: March 14, 2019
Processing time: 90 Days and 7.7 Hours

It was shown in previous studies that high definition endoscopy, high magnification endoscopy and image enhancement technologies, such as chromoendoscopy and digital chromoendoscopy [narrow-band imaging (NBI), i-Scan] facilitate the detection and classification of colonic polyps during endoscopic sessions. However, there are no comprehensive studies so far that analyze which endoscopic imaging modalities facilitate the automated classification of colonic polyps. In this work, we investigate the impact of endoscopic imaging modalities on the results of computer-assisted diagnosis systems for colonic polyp staging.


To assess which endoscopic imaging modalities are best suited for the computer-assisted staging of colonic polyps.


In our experiments, we apply twelve state-of-the-art feature extraction methods for the classification of colonic polyps to five endoscopic image databases of colonic lesions. For this purpose, we employ a specifically designed experimental setup to avoid biases in the outcomes caused by differing numbers of images per image database. The image databases were obtained using different imaging modalities. Two databases were obtained by high-definition endoscopy in combination with i-Scan technology (one with chromoendoscopy and one without chromoendoscopy). Three databases were obtained by high-magnification endoscopy (two databases using narrow band imaging and one using chromoendoscopy). The lesions are categorized into non-neoplastic and neoplastic according to the histological diagnosis.


Generally, it is feature-dependent which imaging modalities achieve high results and which do not. For the high-definition image databases, we achieved overall classification rates of up to 79.2% with chromoendoscopy and 88.9% without chromoendoscopy. In the case of the database obtained by high-magnification chromoendoscopy, the classification rates were up to 81.4%. For the combination of high-magnification endoscopy with NBI, results of up to 97.4% for one database and up to 84% for the other were achieved. Non-neoplastic lesions were classified more accurately in general than non-neoplastic lesions. It was shown that the image recording conditions highly affect the performance of automated diagnosis systems and partly contribute to a stronger effect on the staging results than the used imaging modality.


Chromoendoscopy has a negative impact on the results of the methods. NBI is better suited than chromoendoscopy. High-definition and high-magnification endoscopy are equally suited.

Keywords: Endoscopy, Colonic polyps, Automated diagnosis system, Narrow-band imaging, Chromoendoscopy, Imaging modalities, Image enhancement technologies

Core tip: To determine which endoscopic imaging modalities are most suited for the automated diagnosis of colonic polyps, we apply a high number of state-of-the-art diagnosis systems to 5 endoscopic image databases obtained by different imaging modalities. We show that narrow-band imaging is well suited, whereas chromoendoscopy clearly decreases the results. High-definition and high-magnification endoscopy perform equally well. The image recording conditions have a great impact on the performance of the automated diagnosis systems.