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.
Highlights
Optical Coherence Tomography (OCT), which was first demonstrated by the MIT group in 1991 [1], is a powerful medical imaging technique
It has been shown that gastrointestinal endoscopic OCT can visualize multiple esophageal tissue layers and pathological changes in a variety of esophageal diseases, such as eosinophilic esophagitis (EoE), Barrett’s esophagus (BE) and even esophageal cancer [6,7,8]
The proposed enhanced graph search (EEGS) segmentation framework was tested on esophageal OCT images of guinea pigs, which were acquired by an 800-nm ultrahigh resolution gastrointestinal endoscopic OCT system [9, 10, 47]
Summary
Optical Coherence Tomography (OCT), which was first demonstrated by the MIT group in 1991 [1], is a powerful medical imaging technique It can generate high-resolution, non-invasive, 3D images of biological tissues in real time. The OCT image of BE has an irregular mucosal surface and may present an absence of the layered architecture [12]; the OCT image of EoE is featured with increased basal zone thickness in the esophagus [11]. These diseased features can be detected provided that the esophageal OCT images are accurately segmented
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