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

Meng Gan; Cong Wang; Ting Yang; Na Yang; Miao Zhang; Wu Yuan; Xingde Li; Lirong Wang

doi:10.1364/BOE.9.004481

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

Biomed Opt Express. 2018 Aug 27;9(9):4481-4495. doi: 10.1364/BOE.9.004481. eCollection 2018 Sep 1.

Authors

Meng Gan^{1

2}, Cong Wang², Ting Yang¹, Na Yang¹, Miao Zhang¹, Wu Yuan³, Xingde Li³, Lirong Wang¹

Affiliations

¹ Department of Electronic and Information Engineering, Soochow University, Suzhou 215006, China.
² Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.
³ Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA.

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.

Keywords: (100.0100) Image processing; (100.2960) Image analysis; (170.4500) Optical coherence tomography; (170.4580) Optical diagnostics for medicine.

Abstract

Grants and funding