Isfahan University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! On (X network) By
Automatic Esophagus Z-Line Delineation in Endoscopic Images Using a New Boundary Linking Method Publisher



Aghanouri M1, 2 ; Dadashi Serej N2 ; Rabbani H2 ; Adibi P3
Authors
Show Affiliations
Authors Affiliations
  1. 1. Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
  2. 2. Medical Image and Signal Processing Research Center (MISP), School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
  3. 3. Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

Source: IET Image Processing Published:2022


Abstract

Due to the American cancer society, many people with esophageal adenocarcinoma are not survived. The treatment rate can be significant in the early detection of Barrett's esophagus (BE) as a premalignant stage for adenocarcinoma. An important landmark to detect BE is the Z-line. BE segmentation is already highly dependent upon the operator's knowledge and skill. The main aim of this study is automatic Z-line extraction using endoscopic images leading to segmentation of the early BE stage. To this end, a computer-aided detection method exploiting k-means clustering, image segmentation using the edge detector, and a novel boundary linking algorithm is proposed. For the evaluation, the gold standard is considered the average contours of Z-lines extracted by the three experts. The proposed method annotated the Z-line with the accuracy and precision of 0.92 and 0.87, respectively, and the value of the average boundary distance is 5.9 pixels. To the results and visual inspection, the presented method can be used for efficient and robust extraction of the Z-line at the early BE stage. Furthermore, it can be used in other medical imaging applications with complex boundaries and low contrast in the images, limiting the common automatic boundary detection methods. © 2022 The Authors. IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.