Adaptive enhancement of cataractous retinal images for contrast standardization
© 2023. International Federation for Medical and Biological Engineering..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:62 |
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| Enthalten in: |
Medical & biological engineering & computing - 62(2024), 2 vom: 17. Jan., Seite 357-369 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Yang, Bingyu [VerfasserIn] |
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| Links: |
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| Themen: |
Adaptive enhancement |
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| Anmerkungen: |
Date Completed 18.01.2024 Date Revised 18.01.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1007/s11517-023-02937-5 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM363412956 |
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| 520 | |a © 2023. International Federation for Medical and Biological Engineering. | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Adaptive enhancement | |
| 650 | 4 | |a Blurriness grading | |
| 650 | 4 | |a Contrast standardization | |
| 650 | 4 | |a Retinal image enhancement | |
| 700 | 1 | |a Cao, Lvchen |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, He |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Huiqi |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Hanruo |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Ningli |e verfasserin |4 aut | |
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