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Adaptive enhancement of cataractous retinal images for contrast standardization

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机构: [1]Beijing Institute of Technology, Beijing, 100081, China. [2]School of Artificial Intelligence, Henan University, Zhengzhou, 450046, China. [3]Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, China.
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关键词: Retinal image enhancement Contrast standardization Blurriness grading Adaptive enhancement

摘要:
Cataract affects the quality of fundus images, especially the contrast, due to lens opacity. In this paper, we propose a scheme to enhance different cataractous retinal images to the same contrast as normal images, which can automatically choose the suitable enhancement model based on cataract grading. A multi-level cataract dataset is constructed via the degradation model with quantified contrast. Then, an adaptive enhancement strategy is introduced to choose among three enhancement networks based on a blurriness classifier. The blurriness grading loss is proposed in the enhancement models to further constrain the contrast of the enhanced images. During test, the well-trained blurriness classifier can assist in the selection of enhancement networks with specific enhancement ability. Our method performs the best on the synthetic paired data on PSNR, SSIM, and FSIM and has the best PIQE and FID on 406 clinical fundus images. There is a 7.78% improvement for our method compared with the second on the introduced [Formula: see text] score without over-enhancement according to [Formula: see text], which demonstrates that the proper enhancement by our method is close to the high-quality images. The visual evaluation on multiple clinical datasets also shows the applicability of our method for different blurriness. The proposed method can benefit clinical diagnosis and improve the performance of computer-aided algorithms such as vessel tracking and vessel segmentation.© 2023. International Federation for Medical and Biological Engineering.

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出版当年[2023]版:
大类 | 4 区 医学
小类 | 2 区 数学与计算生物学 4 区 计算机:跨学科应用 4 区 工程:生物医学 4 区 医学:信息
最新[2023]版:
大类 | 4 区 医学
小类 | 2 区 数学与计算生物学 4 区 计算机:跨学科应用 4 区 工程:生物医学 4 区 医学:信息
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出版当年[2022]版:
Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Q3 ENGINEERING, BIOMEDICAL Q3 MEDICAL INFORMATICS
最新[2023]版:
Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Q3 ENGINEERING, BIOMEDICAL Q3 MEDICAL INFORMATICS

影响因子: 最新[2023版] 最新五年平均 出版当年[2022版] 出版当年五年平均 出版前一年[2021版] 出版后一年[2023版]

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第一作者机构: [1]Beijing Institute of Technology, Beijing, 100081, China.
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