Diabetic Retinopathy Early Detection Based on OCT and OCTA Feature Fusion

Ahmed Eltanboly; Nabila Eladawi; Mohammed Elmogy; Mohammed Ghazal; Luay Fraiwan; A. Aboelfetouh; A. Riad; R. Keynton; M. El-Azab; S. Schaal; H. Sandhu; A. El-Baz

doi:10.1109/ISSPIT.2018.8642617

Diabetic Retinopathy Early Detection Based on OCT and OCTA Feature Fusion

Ahmed Eltanboly, Nabila Eladawi, Mohammed Elmogy, Mohammed Ghazal, Luay Fraiwan, A. Aboelfetouh, A. Riad, R. Keynton, M. El-Azab, S. Schaal, H. Sandhu, A. El-Baz

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

Diabetic retinopathy (DR) is a leading cause of vision loss in adults between 20 and 74 years. It is one of the significant causes of blindness worldwide. It affects the blood vessels in the retina as a complication of diabetes. Early detection of DR could reduce the severity of the disease and help ophthalmologists in treating and investigating it more efficiently. In this paper, a computer-aided diagnosis (CAD) system was developed for the detection of the early signs of DR using both optical coherence tomography angiography (OCTA) and optical coherence tomography (OCT) scans. This system was able to segment the blood vessels from OCTA images and twelve retinal layers from OCT images. Then, five different features were extracted from both segmented images. Finally, these features are fed to a support vector machines (SVM) to generate a CAD system for the early detection of DR. Using 2-fold cross-validation, this system achieved an average accuracy of 97%.

Original language	English (US)
Title of host publication	2018 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	621-625
Number of pages	5
ISBN (Electronic)	9781538675687
DOIs	https://doi.org/10.1109/ISSPIT.2018.8642617
State	Published - Jul 2 2018
Event	2018 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2018 - Louisville, United States Duration: Dec 6 2018 → Dec 8 2018

Publication series

Name	2018 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2018

Conference

Conference	2018 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2018
Country/Territory	United States
City	Louisville
Period	12/6/18 → 12/8/18

Keywords

Computer-aided Diagnosis (CAD)
Diabetic Retinopathy (DR)
Feature fusion
Optical Coherence Tomography (OCT)
Optical Coherence Tomography Angiography (OCTA)

ASJC Scopus subject areas

Signal Processing
Computer Science Applications
Computer Networks and Communications

Access to Document

10.1109/ISSPIT.2018.8642617

Cite this

Eltanboly, A., Eladawi, N., Elmogy, M., Ghazal, M., Fraiwan, L., Aboelfetouh, A., Riad, A., Keynton, R., El-Azab, M., Schaal, S., Sandhu, H., & El-Baz, A. (2018). Diabetic Retinopathy Early Detection Based on OCT and OCTA Feature Fusion. In 2018 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2018 (pp. 621-625). Article 8642617 (2018 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISSPIT.2018.8642617

Diabetic Retinopathy Early Detection Based on OCT and OCTA Feature Fusion. / Eltanboly, Ahmed; Eladawi, Nabila; Elmogy, Mohammed et al.
2018 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 621-625 8642617 (2018 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Eltanboly, A, Eladawi, N, Elmogy, M, Ghazal, M, Fraiwan, L, Aboelfetouh, A, Riad, A, Keynton, R, El-Azab, M, Schaal, S, Sandhu, H & El-Baz, A 2018, Diabetic Retinopathy Early Detection Based on OCT and OCTA Feature Fusion. in 2018 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2018., 8642617, 2018 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2018, Institute of Electrical and Electronics Engineers Inc., pp. 621-625, 2018 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2018, Louisville, United States, 12/6/18. https://doi.org/10.1109/ISSPIT.2018.8642617

Eltanboly A, Eladawi N, Elmogy M, Ghazal M, Fraiwan L, Aboelfetouh A et al. Diabetic Retinopathy Early Detection Based on OCT and OCTA Feature Fusion. In 2018 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 621-625. 8642617. (2018 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2018). doi: 10.1109/ISSPIT.2018.8642617

Eltanboly, Ahmed ; Eladawi, Nabila ; Elmogy, Mohammed et al. / Diabetic Retinopathy Early Detection Based on OCT and OCTA Feature Fusion. 2018 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 621-625 (2018 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2018).

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AB - Diabetic retinopathy (DR) is a leading cause of vision loss in adults between 20 and 74 years. It is one of the significant causes of blindness worldwide. It affects the blood vessels in the retina as a complication of diabetes. Early detection of DR could reduce the severity of the disease and help ophthalmologists in treating and investigating it more efficiently. In this paper, a computer-aided diagnosis (CAD) system was developed for the detection of the early signs of DR using both optical coherence tomography angiography (OCTA) and optical coherence tomography (OCT) scans. This system was able to segment the blood vessels from OCTA images and twelve retinal layers from OCT images. Then, five different features were extracted from both segmented images. Finally, these features are fed to a support vector machines (SVM) to generate a CAD system for the early detection of DR. Using 2-fold cross-validation, this system achieved an average accuracy of 97%.

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Diabetic Retinopathy Early Detection Based on OCT and OCTA Feature Fusion

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