Inpainting for Saturation Artifacts in Optical Coherence Tomography Using Dictionary-Based Sparse Representation
Inpainting for Saturation Artifacts in Optical Coherence Tomography Using Dictionary-Based Sparse Representation
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DOI：
10.1109/jphot.2021.3056574
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发表时间：
2021-04-01
影响因子：
2.4
通讯作者：
Gan, Yu
中科院分区：
文献类型：
--
作者：
Liu, Hongshan;Cao, Shengting;Gan, Yu
光学相干层析成像（OCT）中，当接收信号超过光谱仪的动态范围时，会出现饱和伪影。饱和伪影显示条纹图案，并可能影响OCT图像的质量，导致不准确的医疗诊断。在本文中，我们自动定位饱和度的文物，并提出了一种文物校正方法，通过修复。我们采用基于字典的稀疏表示方案进行修复。实验结果表明，无论是合成伪影还是真实的伪影，该方法在定性和定量上都优于插值方法和欧拉弹性方法。当应用于从字典训练中排除的组织样本时，通用字典提供类似的图像质量。该方法有可能广泛应用于各种OCT图像中，用于饱和伪影的定位和修复。
Saturation artifacts in optical coherence tomography (OCT) occur when received signal exceeds the dynamic range of spectrometer. Saturation artifact shows a streaking pattern and could impact the quality of OCT images, leading to inaccurate medical diagnosis. In this paper, we automatically localize saturation artifacts and propose an artifact correction method via inpainting. We adopt a dictionary-based sparse representation scheme for inpainting. Experimental results demonstrate that, in both case of synthetic artifacts and real artifacts, our method outperforms interpolation method and Euler's elastica method in both qualitative and quantitative results. The generic dictionary offers similar image quality when applied to tissue samples which are excluded from dictionary training. This method may have the potential to be widely used in a variety of OCT images for the localization and inpainting of the saturation artifacts.