Mr. Xudong Chen Profile

Xudong Chen

at Beijing Information Sci and Tech University

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Proceedings Article | 27 November 2023 Poster + Paper

Fast motion correction in optical coherence tomography with image feature matching

Xudong Chen, Zongqing Ma, Jiang Zhu, Chongyang Wang

Proceedings Volume 12770, 127701U (2023) https://doi.org/10.1117/12.2686448

KEYWORDS: Optical coherence tomography

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Optical coherence tomography (OCT) is a non-invasive, label-free imaging technique with high resolution. Due to the relatively low scan rate of OCT and involuntary bulk motion of tissues, the OCT image will be distorted by the motion artifacts. The motion artifacts can be reduced by hardware and software methods. In hardware methods, additional hardware is used to track the motion of the object, and extra scans may be required during data acquisition. The software methods can simplify the device and the data acquisition. However, the motion correction based on the cross-correlation analysis is time-consuming. In this study, we proposed a fast motion correction method for OCT images based on image feature matching. First, the motion-related mismatch in the slow scan direction was compensated by the image feature matching between the adjacent B-scans based on the oriented FAST and rotated BRIEF (ORB) approach. Then the axial motion in A-scans was corrected by the boundary detection of the tissue structure and the non-rigid transformation between the corresponding A-lines in the adjacent B-scans. The fast motion correction method was validated by the OCT imaging of a rat ear. The results show that the method can effectively correct motion artifacts of OCT images with a fast processing speed.

Proceedings Article | 19 December 2022 Poster

Unsupervised speckle reduction of retinal optical coherence tomography images via edge-enhanced generative adversarial network

Zongqing Ma, Jiawei Ma, Xudong Chen, Xiaochen Meng, Jiang Zhu

Proceedings Volume 12320, 123201J (2022) https://doi.org/10.1117/12.2642418

KEYWORDS: Optical coherence tomography, Speckle, Performance modeling, Eye models, Data modeling, Image quality, Image enhancement, Image denoising, Denoising

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Speckle noise in optical coherence tomography (OCT) images seriously degrades the image quality and impairs the subsequent diagnosis of various ocular diseases. Most of the existing deep learning-based denoising models pay little attention to edge preservation, and rely on the large number of reference clean images which are hard to acquire in clinical OCT practice. In this work, an unsupervised retinal OCT image denoising model, named as edge-enhanced generative adversarial network (EEGAN), is proposed to free the dependence on reference clean images and enhance the edge information. Specifically, considering the noisy OCT image can be roughly divided into noisy retinal foreground and noise-only background regions, the generator of EEGAN is designed to denoise the noisy foreground samples based on the residual dense blocks, while the discriminator of EEGAN is employed to distinguish the real background noise samples from the fake noise samples, i.e., the difference images between the noisy foreground samples and its generated counterparts. As retinal edge details are the most vital information for disease diagnosis, an edge enhancement layer based on Sobel operators is integrated into the generator of EEGAN to strengthen the edge preservation ability of the model. Experimental results on clinical retinal OCT datasets show that our model has a better performance than the compared models in suppressing noise and preserving details, demonstrating the effectiveness of the proposed EEGAN.

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