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Adaptive enhancement of cataractous retinal images for contrast standardization

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Abstract

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 \(P_{h}\) score without over-enhancement according to \(P_{oe}\), 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.

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Funding

This work was supported by the National Natural Science Foundation of China (No. 82072007), the Key Scientific Research Projects of Colleges and Universities in Henan Province (No. 23A520011), and Key R &D and Promotion Projects of Henan Province (No. 232102211089).

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Authors and Affiliations

  1. Beijing Institute of Technology, Beijing, 100081, China

    Bingyu Yang, He Zhao & Huiqi Li

  2. School of Artificial Intelligence, Henan University, Zhengzhou, 450046, China

    Lvchen Cao

  3. Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, China

    Hanruo Liu & Ningli Wang

Authors
  1. Bingyu Yang
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  2. Lvchen Cao
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  3. He Zhao
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  4. Huiqi Li
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Correspondence to Huiqi Li.

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Yang, B., Cao, L., Zhao, H. et al. Adaptive enhancement of cataractous retinal images for contrast standardization. Med Biol Eng Comput 62, 357–369 (2024). https://doi.org/10.1007/s11517-023-02937-5

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  • DOI: https://doi.org/10.1007/s11517-023-02937-5

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