Inpainting for Saturation Artifacts in Optical Coherence Tomography Using Dictionary-Based Sparse Representation

Liu, Hongshan; Cao, Shengting; Ling, Yuye; Gan, Yu

doi:10.1109/jphot.2021.3056574

Cited by 13 publications

(8 citation statements)

References 22 publications

(29 reference statements)

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“…If the image has a higher PSNR value, then the quality of the compressed or reconstructed image is better. It is expressed in Equation 12 used to evaluate the MIC-DWT-CNN compression method, where maxpixel denotes the highest possible pixel value of the image matrix (Liu H. et al, 2021). We experimented with the MIC-DWT-CNN method on the medical images and demonstrated its improved image compression efficiency.…”

Section: 𝐶𝑜𝑚𝑝𝑟𝑒𝑠𝑠𝑖𝑜𝑛𝑅𝑎𝑡𝑖𝑜 =mentioning

confidence: 99%

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2022

JER is an international, peer-reviewed journal that publishes f

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Section: 𝐶𝑜𝑚𝑝𝑟𝑒𝑠𝑠𝑖𝑜𝑛𝑅𝑎𝑡𝑖𝑜 =mentioning

confidence: 99%

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2022

JER is an international, peer-reviewed journal that publishes f

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“…To further address these issues, we plan to perform detection, removal, and inpainting for the artifact regions and tackle the label noise in the future [46,47].…”

Section: The Impact Of Imaging Artifacts and Label Noises On The Segm...mentioning

confidence: 99%

Multi-scale GCN-assisted two-stage network for joint segmentation of retinal layers and disc in peripapillary OCT images

Li¹,

Jin²,

Zhu³

et al. 2021

Preprint

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An accurate and automated tissue segmentation algorithm for retinal optical coherence tomography (OCT) images is crucial for the diagnosis of glaucoma. However, due to the presence of the optic disc, the anatomical structure of the peripapillary region of the retina is complicated and is challenging for segmentation. To address this issue, we developed a novel graph convolutional network (GCN)-assisted two-stage framework to simultaneously label the nine retinal layers and the optic disc. Specifically, a multi-scale global reasoning module is inserted between the encoder and decoder of a U-shape neural network to exploit anatomical prior knowledge and perform spatial reasoning. We conducted experiments on human peripapillary retinal OCT images. The Dice score of the proposed segmentation network is 0.820 ± 0.001 and the pixel accuracy is 0.830 ± 0.002, both of which outperform those from other state-of-the-art techniques.

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“…12 Liu et al proposed a dictionary-based sparse representation method recently for saturation artifacts removal in OCT images. 13 While their method works very well with saturation artifacts narrower than the patch width, it appears to be less effective when large areas of shadow are presented.…”

Section: Introductionmentioning

confidence: 99%

Multi-scale Sparse Representation-Based Shadow Inpainting for Retinal OCT Images

Tang¹,

Li²,

Liu³

et al. 2022

Preprint

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Inpainting shadowed regions cast by superficial blood vessels in retinal optical coherence tomography (OCT) images is critical for accurate and robust machine analysis and clinical diagnosis. Traditional sequence-based approaches such as propagating neighboring information to gradually fill in the missing regions are cost-effective. But they generate less satisfactory outcomes when dealing with larger missing regions and texture-rich structures. Emerging deep learning-based methods such as encoder-decoder networks have shown promising results in natural image inpainting tasks. However, they typically need a long computational time for network training in addition to the high demand on the size of datasets, which makes it difficult to be applied on often small medical datasets. To address these challenges, we propose a novel multi-scale shadow inpainting framework for OCT images by synergically applying sparse representation and deep learning: sparse representation is used to extract features from a small amount of training images for further inpainting and to regularize the image after the multi-scale image fusion, while convolutional neural network (CNN) is employed to enhance the image quality. During the image inpainting, we divide preprocessed input images into different branches based on the shadow width to harvest complementary information from different scales. Finally, a sparse representation-based regularizing module is designed to refine the generated contents after multi-scale feature aggregation. Experiments are conducted to compare our proposal versus both traditional and deep learning-based techniques on synthetic and real-world shadows. Results demonstrate that our proposed method achieves favorable image inpainting in terms of visual quality and quantitative metrics, especially when wide shadows are presented.

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Inpainting for Saturation Artifacts in Optical Coherence Tomography Using Dictionary-Based Sparse Representation

Cited by 13 publications

References 22 publications

Untitled

Untitled

Multi-scale GCN-assisted two-stage network for joint segmentation of retinal layers and disc in peripapillary OCT images

Multi-scale Sparse Representation-Based Shadow Inpainting for Retinal OCT Images

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