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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.

Titel:	Adaptive enhancement of cataractous retinal images for contrast standardization.
Autor/in / Beteiligte Person:	Yang, B ; Cao, L ; Zhao, H ; Li, H ; Liu, H ; Wang, N
Link:	Full Text Finder (Volltext)
Zeitschrift:	Medical & biological engineering & computing, Jg. 62 (2024-02-01), Heft 2, S. 357-369
Veröffentlichung:	New York, NY : Springer ; <i>Original Publication</i>: Stevenage, Eng., Peregrinus., 2024
Medientyp:	academicJournal
ISSN:	1741-0444 (electronic)
DOI:	10.1007/s11517-023-02937-5
Schlagwort:	Humans Fundus Oculi Retinal Vessels diagnostic imaging Reference Standards Image Processing, Computer-Assisted methods Algorithms Cataract diagnostic imaging
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article Language: English [Med Biol Eng Comput] 2024 Feb; Vol. 62 (2), pp. 357-369. <i>Date of Electronic Publication: </i>2023 Oct 18. MeSH Terms: Algorithms* ; Cataract* / diagnostic imaging ; Humans ; Fundus Oculi ; Retinal Vessels / diagnostic imaging ; Reference Standards ; Image Processing, Computer-Assisted / methods References: Zhang J, Li H, Nie Q, Cheng L (2014) A retinal vessel boundary tracking method based on Bayesian theory and multi-scale line detection. Comput Med Imaging Graph 38(6):517–525. (PMID: 10.1016/j.compmedimag.2014.05.01024974011) ; Cao L, Li H, Zhang Y, Zhang L, Xu L (2020) Hierarchical method for cataract grading based on retinal images using improved Haar wavelet. Information Fusion 53:196–208. (PMID: 10.1016/j.inffus.2019.06.022) ; Staal J, Abrámoff MD, Niemeijer M, Viergever MA, Van Ginneken B (2004) Ridge-based vessel segmentation in color images of the retina. 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Adaptive enhancement of cataractous retinal images for contrast standardization.