Presentation
19 April 2017 Speckle reduction in optical coherence tomography using two-step iteration method (Conference Presentation)
Author Affiliations +
Proceedings Volume 10057, Multimodal Biomedical Imaging XII; 1005702 (2017) https://doi.org/10.1117/12.2250832
Event: SPIE BiOS, 2017, San Francisco, California, United States
Abstract
Optical coherence tomography (OCT) provides high resolution and cross-sectional images of biological tissue and is widely used for diagnosis of ocular diseases. However, OCT images suffer from speckle noise, which typically considered as multiplicative noise in nature, reducing the image resolution and contrast. In this study, we propose a two-step iteration (TSI) method to suppress those noises. We first utilize augmented Lagrange method to recover a low-rank OCT image and remove additive Gaussian noise, and then employ the simple and efficient split Bregman method to solve the Total-Variation Denoising model. We validated such proposed method using images of swine, rabbit and human retina. Results demonstrate that our TSI method outperforms the other popular methods in achieving higher peak signal-to-noise ratio (PSNR) and structure similarity (SSIM) while preserving important structural details, such as tiny capillaries and thin layers in retinal OCT images. In addition, the results of our TSI method show clearer boundaries and maintains high image contrast, which facilitates better image interpretations and analyses.
Conference Presentation
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xianghong Wang, Xinyu Liu Sr., Nanshuo Wang, Xiaojun Yu, En Bo, Si Chen M.D., and Linbo Liu "Speckle reduction in optical coherence tomography using two-step iteration method (Conference Presentation)", Proc. SPIE 10057, Multimodal Biomedical Imaging XII, 1005702 (19 April 2017); https://doi.org/10.1117/12.2250832
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KEYWORDS
Optical coherence tomography

Speckle

Image resolution

Signal to noise ratio

Capillaries

Denoising

Image analysis

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