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Digital image processing software for diagnosing diabetic retinopathy from fundus photograph

Authors Ratanapakorn T, Daengphoonphol A, Eua-Anant N, Yospaiboon Y

Received 22 November 2018

Accepted for publication 20 February 2019

Published 17 April 2019 Volume 2019:13 Pages 641—648

DOI https://doi.org/10.2147/OPTH.S195617

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Cristina Weinberg

Peer reviewer comments 2

Editor who approved publication: Dr Scott Fraser


Tanapat Ratanapakorn,1 Athiwath Daengphoonphol,2 Nawapak Eua-Anant,2 Yosanan Yospaiboon1

1KKU Eye Center, Department of Ophthalmology, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand; 2Department of Computer Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen, Thailand

Objective: The aim of this study was to develop automated software for screening and diagnosing diabetic retinopathy (DR) from fundus photograph of patients with diabetes mellitus.
Methods: The extraction of clinically significant features to detect pathologies of DR and the severity classification were performed by using MATLAB R2015a with MATLAB Image Processing Toolbox. In addition, the graphic user interface was developed using the MATLAB GUI Toolbox. The accuracy of software was measured by comparing the obtained results to those of the diagnosis by the ophthalmologist.
Results: A set of 400 fundus images, containing 21 normal fundus images and 379 DR fundus images (162 non-proliferative DR and 217 proliferative DR), was interpreted by the ophthalmologist as a reference standard. The initial result showed that the sensitivity, specificity and accuracy of this software in detection of DR were 98%, 67% and 96.25%, respectively. However, the accuracy of this software in classifying non-proliferative and proliferative diabetic retinopathy was 66.58%. The average time for processing is 7 seconds for one fundus image.
Conclusion: The automated DR screening software was developed by using MATLAB programming and yielded 96.25% accuracy for the detection of DR when compared to that of the diagnosis by the ophthalmologist. It may be a helpful tool for DR screening in the distant rural area where ophthalmologist is not available.

Keywords: automated diabetic retinopathy software, diabetic retinopathy screening, fundus photography diagnosis, digital image processing


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