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30 March 2023 Multiscale denoising generative adversarial network for speckle reduction in optical coherence tomography images
Xiaojun Yu, Chenkun Ge, Mingshuai Li, Muhammad Zulkifal Aziz, Jianhua Mo, Zeming Fan
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Journal of Medical Imaging, Vol. 10, Issue 2, 024006 (March 2023). https://doi.org/10.1117/1.JMI.10.2.024006
Abstract

Purpose

Optical coherence tomography (OCT) is a noninvasive, high-resolution imaging modality capable of providing both cross-sectional and three-dimensional images of tissue microstructures. Owing to its low-coherence interferometry nature, however, OCT inevitably suffers from speckles, which diminish image quality and mitigate the precise disease diagnoses, and therefore, despeckling mechanisms are highly desired to alleviate the influences of speckles on OCT images.

Approach

We propose a multiscale denoising generative adversarial network (MDGAN) for speckle reductions in OCT images. A cascade multiscale module is adopted as MDGAN basic block first to raise the network learning capability and take advantage of the multiscale context, and then a spatial attention mechanism is proposed to refine the denoised images. For enormous feature learning in OCT images, a deep back-projection layer is finally introduced to alternatively upscale and downscale the features map of MDGAN.

Results

Experiments with two different OCT image datasets are conducted to verify the effectiveness of the proposed MDGAN scheme. Results compared those of the state-of-the-art existing methods show that MDGAN is able to improve both peak-single-to-noise ratio and signal-to-noise ratio by 3 dB at most, with its structural similarity index measurement and contrast-to-noise ratio being 1.4% and 1.3% lower than those of the best existing methods.

Conclusions

Results demonstrate that MDGAN is effective and robust for OCT image speckle reductions and outperforms the best state-of-the-art denoising methods in different cases. It could help alleviate the influence of speckles in OCT images and improve OCT imaging-based diagnosis.
© 2023 Society of Photo-Optical Instrumentation Engineers (SPIE)
Citation Download Citation
Xiaojun Yu, Chenkun Ge, Mingshuai Li, Muhammad Zulkifal Aziz, Jianhua Mo, and Zeming Fan "Multiscale denoising generative adversarial network for speckle reduction in optical coherence tomography images," Journal of Medical Imaging 10(2), 024006 (30 March 2023). https://doi.org/10.1117/1.JMI.10.2.024006
Received: 4 August 2022; Accepted: 13 March 2023; Published: 30 March 2023
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KEYWORDS
Optical coherence tomography
Image processing
Denoising
Speckle
Education and training
Signal to noise ratio
Optical coherence
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