Understanding, Modeling, and Correcting Low-quality Retinal Fundus Images for Clinical Observation and Analysis

Ziyi Shen, Huazhu Fu, Jianbin Shen, and Ling Shao

Retinal fundus image dataset🚀🚀🚀

In this work, we analyze the ophthalmoscope imaging system and model the reliable degradation of major inferior-quality factors, including uneven illumination, blur, and artifacts.

Fundus image correction algorithm 💭💭💭

Based on the proposed realistic degradation model, a clinical-oriented fundus correction network (Cofe-Net) is proposed to suppress the global degradation factors, and simulataneously preserve anatomical retinal structure and pathological characteristics for clinical observation and analysis. This algorithm is able to effectively corrects low-quality fundus images without losing retinal details, and benefits medical image analysis applications, e.g., retinal vessel segmentatio and optic disc/cup detection.

Here we will release the code of our degradation algorithm and corresponding parameters for low-quality fundus image generation. You also could refer to your own requirement and simulate specific images by setting your own data.

The correction code has been released here: https://github.com/joanshen0508/Fundus-correction-cofe-Net

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Reference:

[1] Ziyi Shen, Huazhu Fu, Jianbing Shen, and Ling Shao, "Modeling and Enhancing Low-quality Retinal Fundus Images", IEEE TMI, 2021. [arXiv]

[2] Huazhu Fu, Boyang Wang, Jianbing Shen, Shanshan Cui, Yanwu Xu, Jiang Liu, and Ling Shao, "Evaluation of Retinal Image Quality Assessment Networks in Different Color-spaces", in International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019. [arXiv] [data and code]