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Fundus image quality assessment: survey, challenges, and future scope

机译：眼底图像质量评估：调查，挑战和未来范围

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摘要

Various ocular diseases, such as cataract, diabetic retinopathy, and glaucoma have affected a large proportion of the population worldwide. In ophthalmology, fundus photography is used for the diagnosis of such retinal disorders. Nowadays, the set-up of fundus image acquisition has changed from a fixed position to portable devices, making acquisition more vulnerable to distortions. However, a trustworthy diagnosis solely relies upon the quality of the fundus image. In recent years, fundus image quality assessment (IQA) has drawn much attention from researchers. This study presents a detailed survey of the fundus IQA research. The survey covers a comprehensive discussion on the factors affecting the fundus image quality and the real-time distortions. The fundus IQA algorithms have been analysed on the basis of the methodologies used and divided into three classes, namely: (i) similarity-based, (ii) segmentation-based, and (iii) machine learning based. In addition, limitations of state of the art in this research field are also presented with the possible solutions. The objective of this study is to provide a detailed information about the fundus IQA research with its significance, present status, limitations, and future scope. To the best of the authors' knowledge, this is the first survey paper on the fundus IQA research.
机译：白内障，糖尿病性视网膜病变和青光眼等各种眼科疾病已影响到全球很大比例的人口。在眼科中，眼底照相术被用于诊断这种视网膜疾病。如今，眼底图像采集的设置已从固定位置更改为便携式设备，从而使采集更加容易受到失真的影响。但是，可靠的诊断仅取决于眼底图像的质量。近年来，眼底图像质量评估（IQA）引起了研究人员的广泛关注。这项研究提供了对眼底IQA研究的详细调查。该调查涵盖了有关影响眼底图像质量和实时失真的因素的全面讨论。已根据所使用的方法对眼底IQA算法进行了分析，并将其分为三类：（i）基于相似度，（ii）基于分段和（iii）基于机器学习。此外，还提出了可能的解决方案，以限制该研究领域的技术发展水平。这项研究的目的是提供有关眼底IQA研究的详细信息，包括其意义，现状，局限性和未来范围。据作者所知，这是有关眼底IQA研究的第一篇调查论文。

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