Diabetic retinopathy assessment: Towards an automated system


Wan Zaki, Wan Mimi Diyana and Zulkifley, M. Asyraf and Hussain, Aini and Wan A. Halim, Wan Haslina and A. Mustafa, N. Badariah and Lim, Sin Ting (2016) Diabetic retinopathy assessment: Towards an automated system. Biomedical Signal Processing and Control, 24. pp. 72-82. ISSN 1746-8094

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The incidence of diabetes and diabetic retinopathy has been shown to be increasing worldwide. While ophthalmologists struggle to treat this retinopathy, they are also faced with an increment of diabetic referrals for eye screening. Screening and early detection of diabetic retinopathy are crucial to help reduce the incidence of visual morbidity and visual loss. In most countries, diabetic retinopathy assessments are done manually. This is time consuming and is a cause of additional clinical workloads. Clinicians are now aware of the need for an automated system for grading Diabetic Retinopathy (DR) that can help in tracing abnormalities in patients’ retinas based on their fundus images, and assist in grading the retina conditions accordingly. This will lead to more effective assessment methods, as well as providing a second opinion to the ophthalmologist during diagnosis. This paper presents an overview of various methods of automated DR grading assessment systems that can complement manual assessments. Tortuosity of the blood vessels is introduced as one of the significant features that can be quantified and associated with DR stages for the grading assessment. From this review, it can be concluded that the automated system has a huge potential for wider acceptance in real life applications. However, there is still some space for improvement for a more robust system. Nevertheless, the DR automated grading assessment system is foreseen as being widely embraced by researchers and ophthalmologists in the future

Item Type: Article
Uncontrolled Keywords: Diabetic retinopathy (DR), Digital fundus image, Tortuosity, Automated DR grading
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Engineering and Technology (FET)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 13 Dec 2017 15:56
Last Modified: 13 Dec 2017 15:56
URII: http://shdl.mmu.edu.my/id/eprint/6627


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