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Retinal image quality assessment for diabetic retinopathy screening: A survey

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Abstract

Retinal image quality assessment (RIQA) is one of the key components in screening for diabetic retinopathy (DR). As one of the most serious complications of diabetes, DR has become a leading cause of blindness in adults globally. DR screening is essential to achieve early diagnosis so that effective treatment could be provided timely. However, the collected images of medically unsatisfactory quality always lead to failure of diagnosis and waste of ophthalmologists’ precious time. Hence, the first step in a good DR screening program is verifying retinal images of good quality. In this paper, we provide a systematic review on automated assessment of retinal image quality for DR screening. Scheme and parameters for RIQA are firstly presented. Next, we provide detailed understanding of the existing RIQA techniques, algorithms and methodologies, including brief description and analysis of each existing state-of-art approaches and comparison between such methods. Datasets and evaluation metrics are also illustrated. Finally, several challenges and future research directions are summarized and discussed.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 41801324), by the Natural Science Foundation of Fujian Province, China (No.2016 J0129), by the Educational Commission of Fujian Province of China (No.JAT160070).

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Authors and Affiliations

  1. College of Physics and Information Engineering, Fuzhou University, Fujian, China

    Jiawen Lin & Lun Yu

  2. College of Mathematics and Computer Science, Fuzhou University, Fujian, China

    Jiawen Lin, Qian Weng & Xianghan Zheng

  3. Fujian Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University), Fujian, China

    Jiawen Lin & Xianghan Zheng

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  1. Jiawen Lin
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Lin, J., Yu, L., Weng, Q. et al. Retinal image quality assessment for diabetic retinopathy screening: A survey. Multimed Tools Appl 79, 16173–16199 (2020). https://doi.org/10.1007/s11042-019-07751-6

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