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Robust layer segmentation of esophageal OCT images based on graph search using edge-enhanced weights
Author(s) -
Meng Gan,
Cong Wang,
Ting Yang,
Na Yang,
Mi Ao Zhang,
Weihao Yuan,
Xi Ngde Li,
Li Rong Wang
Publication year - 2018
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.9.004481
Subject(s) - canny edge detector , preprocessor , computer science , artificial intelligence , image segmentation , segmentation , computer vision , image gradient , pattern recognition (psychology) , robustness (evolution) , edge detection , graph , speckle pattern , speckle noise , image processing , image texture , image (mathematics) , theoretical computer science , biochemistry , chemistry , gene
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.

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