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. 2019 Feb 8;19(3):695.
doi: 10.3390/s19030695.

An Approach to Automatic Hard Exudate Detection in Retina Color Images by a Telemedicine System Based on the d-Eye Sensor and Image Processing Algorithms

Emil Saeed  1 , Maciej Szymkowski  2 , Khalid Saeed  3 , Zofia Mariak  4
Affiliations

An Approach to Automatic Hard Exudate Detection in Retina Color Images by a Telemedicine System Based on the d-Eye Sensor and Image Processing Algorithms

Emil Saeed et al. Sensors (Basel). .

Abstract

Hard exudates are one of the most characteristic and dangerous signs of diabetic retinopathy. They can be marked during the routine ophthalmological examination and seen in color fundus photographs (i.e., using a fundus camera). The purpose of this paper is to introduce an algorithm that can extract pathological changes (i.e., hard exudates) in diabetic retinopathy. This was a retrospective, nonrandomized study. A total of 100 photos were included in the analysis-50 sick and 50 normal eyes. Small lesions in diabetic retinopathy could be automatically diagnosed by the system with an accuracy of 98%. During the experiments, the authors used classical image processing methods such as binarization or median filtration, and data was read from the d-Eye sensor. Sixty-seven patients (39 females and 28 males with ages ranging between 50 and 64) were examined. The results have shown that the proposed solution accuracy level equals 98%. Moreover, the algorithm returns correct classification decisions for high quality images and low quality samples. Furthermore, we consider taking retina photos using mobile phones rather than fundus cameras, which is more practical. The paper presents an innovative approach. The results are introduced and the algorithm is described.

Keywords: automatic diagnosis; d-eye sensor; hard exudates; image processing; retina.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
The architecture of the proposed system.
Figure 2
Figure 2
All devices used during research: (a) d-Eye overlay for smartphone, (b) Kowa VX-10 Fundus Camera, and (c) Digital Eye Center Microclear Handheld Ophthalmic Camera HNF.
Figure 2
Figure 2
All devices used during research: (a) d-Eye overlay for smartphone, (b) Kowa VX-10 Fundus Camera, and (c) Digital Eye Center Microclear Handheld Ophthalmic Camera HNF.
Figure 3
Figure 3
The activity diagram of the proposed approach.
Figure 4
Figure 4
The block diagram of the image preprocessing algorithm.
Figure 5
Figure 5
Visual comparison between different grayscale conversion methods: (a) original image; (b) green channel; (c) red channel; (d) blue channel; (e) average value of all channels.
Figure 6
Figure 6
(a) Grayscale image and (b) its form after histogram stretching.
Figure 7
Figure 7
Image after (a) histogram stretching and (b) median filtering.
Figure 8
Figure 8
Image (a) after median filtering and (b) after gamma correction.
Figure 9
Figure 9
The block diagram of the proposed retina vascular pattern extraction.
Figure 10
Figure 10
(a) Original image, (b) image after conversion to grayscale with green channel and (c) image after noise removal.
Figure 11
Figure 11
Image after (a) histogram equalization and (b) after brightness correction.
Figure 12
Figure 12
Image after (a) brightness correction and (b) Gaussian matched filter.
Figure 13
Figure 13
Image (a) after binarization and (b) after short vessel removal.
Figure 14
Figure 14
Image (a) after the first step and (b) after vascular pattern removal.
Figure 15
Figure 15
Image (a) after vascular pattern removal and (b) after binarization.
Figure 16
Figure 16
Image (a) after binarization and (b) calculated image variance.
Figure 17
Figure 17
Image after (a) calculating its variance and (b) after optic disk removal.
Figure 18
Figure 18
Retina color image with marked pathological changes.
Figure 19
Figure 19
Retina color image obtained by the device with worse parameters. No pathological changes exist.
See this image and copyright information in PMC

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References

    1. James B., Chew C., Bron A. Lecture Notes. Ophthalmology. 2007;1:172–173.
    1. Klein B.E. Overview of epidemiologic studies of diabetic retinopathy. Ophthalmic Epidemiol. 2007;14:179–183. doi: 10.1080/09286580701396720. - DOI - PubMed
    1. Chen Y., Li Y., Yan Y., Shen X. Diabetic macular morphology changes may occur in the early stage of diabetes. BMC Ophthalmol. 2016;16 doi: 10.1186/s12886-016-0186-4. - DOI - PMC - PubMed
    1. Sasaki M., Kawasaki R., Noonan J.E., Wong T.Y., Lamourex E., Wang J.J. Quantitative measurement of hard exudates in patients with diabetes and their associations with serum lipid levels. Invest. Ophthalmol. Vis. Sci. 2013;54:5544–5550. doi: 10.1167/iovs.13-11849. - DOI - PubMed
    1. Marupally A.G., Vupparaboina K.K., Peguda H.K., Richhariya A., Jana S., Chhablani J. Semi-automated quantification of hard exudates in colour fundus photographs diagnosed with diabetic retinopathy. BMC Ophthalmol. 2017;17:172. doi: 10.1186/s12886-017-0563-7. - DOI - PMC - PubMed