Optical coherence tomography (OCT) has been widely applied in the examination and diagnosis of corneal diseases, but the information directly achieved from the OCT images by manual inspection is limited. We propose an automatic processing method to assist ophthalmologists in locating the boundaries in corneal OCT images and analyzing the recovery of corneal wounds after treatment from longitudinal OCT images. It includes the following steps: preprocessing, epithelium and endothelium boundary segmentation and correction, wound detection, corneal boundary fitting and wound analysis. The method was tested on a data set with longitudinal corneal OCT images from 20 subjects. Each subject has five images acquired after corneal operation over a period of time. The segmentation and classification accuracy of the proposed algorithm is high and can be used for analyzing wound recovery after corneal surgery.
Fei Wang, Fei Shi, Weifang Zhu, Lingjiao Pan, Haoyu Chen, Haifan Huang, Kangkeng Zheng, and Xinjian Chen, "Automated boundary segmentation and wound analysis for longitudinal corneal OCT images," Proc. SPIE 10137, Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1013708 (Presented at SPIE Medical Imaging: February 12, 2017; Published: 13 March 2017); https://doi.org/10.1117/12.2254394.
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