Authors:
K. Yu. Erendzhenova
1
;
O. A. Kulagina
2
;
R. M. Kadushnikov
3
and
T. V. Zarubina
1
Affiliations:
1
Pirogov Russian National Research Medical University, Russian Federation
;
2
Medical Research and Education Center of Lomonosov Moscow State University, Russian Federation
;
3
LLC “SIAMS”, Russian Federation
Keyword(s):
High Definition (HD) Endoscopy, Narrow-Band Imaging (NBI) Endoscopy, Early Gastric Cancer Diagnostics, Decision Support, Pattern Recognition, Endoscopic Image Processing.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Knowledge-Based Systems
;
Pattern Recognition and Machine Learning
;
Symbolic Systems
Abstract:
High Definition (HD) and Magnified Narrow band imaging endoscopy (ME-NBI) allowed to
recognizetypes of gastric lesions according modified VS-classification by professor Yao K., becausethe
parameters to describe regular or irregularvascular or microsurface pattern and demarcation line in
lesionswere formalized. In this work endoscopic differential criteria of benign and neoplastic epithelial
lesions of stomach were obtained. Based on them classification algorithm for the real-time processing of
narrow–band endoscopic images with a highly productive distributed intellectual analytic decision support
system for multiscale endoscopic diagnostics is presented. We also created the electronic atlas and database
to collect high resolution endoscopic images, applied and proved the differential diagnosis of gastric lesions
through the computer analysis. The algorithm consistentlyused scale– invariant feature transform detector,
computation of gastric mucosa pit–pattern skeletons, “Bag
of visual words” method, and K–means method
for key pointsclustering. Resulting classification algorithm is completely automated, performed real-time
analysis, and did not require preliminary selection of interest area. Image classification accuracy was 85%.
(More)