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

Content Provider	IET Digital Library
Author	Raj, Aditya Tiwari, Anil Kumar Martini, Maria G.
Abstract	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.
Starting Page	1211
Ending Page	1224
Page Count	14
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 8, Jun (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.6212
Journal	IET Image Processing
Publisher Date	2019-04-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Biology And Medical Computing Biomedical Imaging/measurement Biomedical Optical Imaging Computer Vision And Image Processing Technique Diseases Eye Fundus Image Acquisition Fundus Image Quality Assessment Fundus IQA Algorithm Fundus Photography Image Segmentation Knowledge Engineering Technique Learning in AI Medical Image Processing Optical And Laser Radiation Optical, Image And Video Signal Processing Patient Diagnostic Method And Instrumentation Photography Physiological Optics Physiological Vision
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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