Abstract
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.
Highlights
Saturation artifacts are commonly observed in optical coherence tomography (OCT), which could degrade the segmentation accuracy [1] and lead to errors in derived clinic parameters [2]
Saturation artifacts in optical coherence tomography (OCT) occur when received signal exceeds the dynamic range of spectrometer
The generic dictionary offers similar image quality when applied to tissue samples which are excluded from dictionary training
Summary
Saturation artifacts are commonly observed in optical coherence tomography (OCT), which could degrade the segmentation accuracy [1] and lead to errors in derived clinic parameters [2]. Those artifacts, featured by streaking patterns, are often caused by specular reflections from the interfaces. The existence of saturation artifacts could hinder the in-depth analysis of OCT images, such as segmentation
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