Precise higher-order reflectivity and morphology models for early diagnosis of diabetic retinopathy using OCT images

Abstract This study proposes a novel computer assisted diagnostic (CAD) system for early diagnosis of diabetic retinopathy (DR) using optical coherence tomography (OCT) B-scans. The CAD system is based on fusing novel OCT markers that describe both the morphology/anatomy and the reflectivity of reti...

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Autores principales:	A. Sharafeldeen, M. Elsharkawy, F. Khalifa, A. Soliman, M. Ghazal, M. AlHalabi, M. Yaghi, M. Alrahmawy, S. Elmougy, H. S. Sandhu, A. El-Baz
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/88e488fc0d1048ff9debd03f81b9764e
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spelling	oai:doaj.org-article:88e488fc0d1048ff9debd03f81b9764e2021-12-02T11:37:27ZPrecise higher-order reflectivity and morphology models for early diagnosis of diabetic retinopathy using OCT images10.1038/s41598-021-83735-72045-2322https://doaj.org/article/88e488fc0d1048ff9debd03f81b9764e2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83735-7https://doaj.org/toc/2045-2322Abstract This study proposes a novel computer assisted diagnostic (CAD) system for early diagnosis of diabetic retinopathy (DR) using optical coherence tomography (OCT) B-scans. The CAD system is based on fusing novel OCT markers that describe both the morphology/anatomy and the reflectivity of retinal layers to improve DR diagnosis. This system separates retinal layers automatically using a segmentation approach based on an adaptive appearance and their prior shape information. High-order morphological and novel reflectivity markers are extracted from individual segmented layers. Namely, the morphological markers are layer thickness and tortuosity while the reflectivity markers are the 1st-order reflectivity of the layer in addition to local and global high-order reflectivity based on Markov-Gibbs random field (MGRF) and gray-level co-occurrence matrix (GLCM), respectively. The extracted image-derived markers are represented using cumulative distribution function (CDF) descriptors. The constructed CDFs are then described using their statistical measures, i.e., the 10th through 90th percentiles with a 10% increment. For individual layer classification, each extracted descriptor of a given layer is fed to a support vector machine (SVM) classifier with a linear kernel. The results of the four classifiers are then fused using a backpropagation neural network (BNN) to diagnose each retinal layer. For global subject diagnosis, classification outputs (probabilities) of the twelve layers are fused using another BNN to make the final diagnosis of the B-scan. This system is validated and tested on 130 patients, with two scans for both eyes (i.e. 260 OCT images), with a balanced number of normal and DR subjects using different validation metrics: 2-folds, 4-folds, 10-folds, and leave-one-subject-out (LOSO) cross-validation approaches. The performance of the proposed system was evaluated using sensitivity, specificity, F1-score, and accuracy metrics. The system’s performance after the fusion of these different markers showed better performance compared with individual markers and other machine learning fusion methods. Namely, it achieved $$96.15\%$$ 96.15 % , $$99.23\%$$ 99.23 % , $$97.66\%$$ 97.66 % , and $$97.69\%$$ 97.69 % , respectively, using the LOSO cross-validation technique. The reported results, based on the integration of morphology and reflectivity markers and by using state-of-the-art machine learning classifications, demonstrate the ability of the proposed system to diagnose the DR early.A. SharafeldeenM. ElsharkawyF. KhalifaA. SolimanM. GhazalM. AlHalabiM. YaghiM. AlrahmawyS. ElmougyH. S. SandhuA. El-BazNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q A. Sharafeldeen M. Elsharkawy F. Khalifa A. Soliman M. Ghazal M. AlHalabi M. Yaghi M. Alrahmawy S. Elmougy H. S. Sandhu A. El-Baz Precise higher-order reflectivity and morphology models for early diagnosis of diabetic retinopathy using OCT images
description	Abstract This study proposes a novel computer assisted diagnostic (CAD) system for early diagnosis of diabetic retinopathy (DR) using optical coherence tomography (OCT) B-scans. The CAD system is based on fusing novel OCT markers that describe both the morphology/anatomy and the reflectivity of retinal layers to improve DR diagnosis. This system separates retinal layers automatically using a segmentation approach based on an adaptive appearance and their prior shape information. High-order morphological and novel reflectivity markers are extracted from individual segmented layers. Namely, the morphological markers are layer thickness and tortuosity while the reflectivity markers are the 1st-order reflectivity of the layer in addition to local and global high-order reflectivity based on Markov-Gibbs random field (MGRF) and gray-level co-occurrence matrix (GLCM), respectively. The extracted image-derived markers are represented using cumulative distribution function (CDF) descriptors. The constructed CDFs are then described using their statistical measures, i.e., the 10th through 90th percentiles with a 10% increment. For individual layer classification, each extracted descriptor of a given layer is fed to a support vector machine (SVM) classifier with a linear kernel. The results of the four classifiers are then fused using a backpropagation neural network (BNN) to diagnose each retinal layer. For global subject diagnosis, classification outputs (probabilities) of the twelve layers are fused using another BNN to make the final diagnosis of the B-scan. This system is validated and tested on 130 patients, with two scans for both eyes (i.e. 260 OCT images), with a balanced number of normal and DR subjects using different validation metrics: 2-folds, 4-folds, 10-folds, and leave-one-subject-out (LOSO) cross-validation approaches. The performance of the proposed system was evaluated using sensitivity, specificity, F1-score, and accuracy metrics. The system’s performance after the fusion of these different markers showed better performance compared with individual markers and other machine learning fusion methods. Namely, it achieved $$96.15\%$$ 96.15 % , $$99.23\%$$ 99.23 % , $$97.66\%$$ 97.66 % , and $$97.69\%$$ 97.69 % , respectively, using the LOSO cross-validation technique. The reported results, based on the integration of morphology and reflectivity markers and by using state-of-the-art machine learning classifications, demonstrate the ability of the proposed system to diagnose the DR early.
format	article
author	A. Sharafeldeen M. Elsharkawy F. Khalifa A. Soliman M. Ghazal M. AlHalabi M. Yaghi M. Alrahmawy S. Elmougy H. S. Sandhu A. El-Baz
author_facet	A. Sharafeldeen M. Elsharkawy F. Khalifa A. Soliman M. Ghazal M. AlHalabi M. Yaghi M. Alrahmawy S. Elmougy H. S. Sandhu A. El-Baz
author_sort	A. Sharafeldeen
title	Precise higher-order reflectivity and morphology models for early diagnosis of diabetic retinopathy using OCT images
title_short	Precise higher-order reflectivity and morphology models for early diagnosis of diabetic retinopathy using OCT images
title_full	Precise higher-order reflectivity and morphology models for early diagnosis of diabetic retinopathy using OCT images
title_fullStr	Precise higher-order reflectivity and morphology models for early diagnosis of diabetic retinopathy using OCT images
title_full_unstemmed	Precise higher-order reflectivity and morphology models for early diagnosis of diabetic retinopathy using OCT images
title_sort	precise higher-order reflectivity and morphology models for early diagnosis of diabetic retinopathy using oct images
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/88e488fc0d1048ff9debd03f81b9764e
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_version_	1718395772241707008

Precise higher-order reflectivity and morphology models for early diagnosis of diabetic retinopathy using OCT images

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