Robust layer segmentation of esophageal OCT images based on graph search using edge-enhanced weights

Abstract

Automatic segmentation of esophageal layers in OCT images is crucial for studying esophageal diseases and computer-assisted diagnosis. This work aims to improve the current techniques to increase the accuracy and robustness for esophageal OCT image segmentation. A two-step edge-enhanced graph search (EEGS) framework is proposed in this study. Firstly, a preprocessing scheme is applied to suppress speckle noise and remove the disturbance in the esophageal structure. Secondly, the image is formulated into a graph and layer boundaries are located by graph search. In this process, we propose an edge-enhanced weight matrix for the graph by combining the vertical gradients with a Canny edge map. Experiments on esophageal OCT images from guinea pigs demonstrate that the EEGS framework is more robust and more accurate than the current segmentation method. It can be potentially useful for the early detection of esophageal diseases.

Fast esophageal layer segmentation in OCT images of guinea pigs based on sparse Bayesian classification and graph search

“…It is worth mentioning that the search region for each boundary is ±6 pixels around the most probable boundary position, which will make the optimization more efficient. The region width is set based on the fact that the smallest tissue layer has a thickness of around 10 µm (11 pixels) [31,32]. In that case, searching a certain boundary in ±6 pixels around the most probable position utilizes as much information as possible without inducing inference from boundaries of other tissue layers.…”

“…To reduce the memory size and improve the algorithm efficiency, the layer search area is constrained to the tissue-related regions. The total thickness of the five tissue layers for the guinea pig is 110 µm (125 pixels) in healthy condition and 150 µm (170 pixels) for the EoE model [31,32]. Considering irregularities, limiting the search region from 5 pixels to 195 pixels below the lumen boundary ( Fig.…”

“…Zhang et al used the graph theory dynamic program (GTDP) to implement five-layer segmentation [31]. On this basis, Gan et al combined modified Canny edge detection with GTDP to realize more accurate segmentation [32]. To the best of our knowledge, the GTDP is the first effective framework to segment five esophageal layers [31].…”

“…In other words, GTDP is difficult to deal with the possible variations in esophageal tissues. Secondly, it takes about 6 to10 seconds to segment a single B-scan [31,32], which is less efficient.…”

Automatic three-dimensional segmentation of endoscopic airway OCT images

“…Recently, we proposed an automatic segmentation method based on the dynamic programming (DP) algorithm to delineate the airway wall and to measure the thickness of the mucosa and submucosa layers [25]. The DP algorithm was also successfully applied to endoscopic esophageal OCT, as presented in [26,27]. However, all these methods are two-dimensional (2D) approaches.…”