An integrated index for the identification of diabetic retinopathy stages using texture parameters

Diabetes is a condition of increase in the blood sugar level higher than the normal range. Prolonged diabetes damages the small blood vessels in the retina resulting in diabetic retinopathy (DR). DR progresses with time without any noticeable symptoms until the damage has occurred. Hence, it is very...

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Main Authors: Acharya, U. Rajendra, Ng, Eddie Yin-Kwee, Tan, Jen Hong, Sree, Subbhuraam Vinitha, Ng, Kwan-Hoong
Other Authors: School of Mechanical and Aerospace Engineering
Format: Article
Language:English
Published: 2013
Online Access:https://hdl.handle.net/10356/100151
http://hdl.handle.net/10220/13593
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1001512020-03-07T13:19:29Z An integrated index for the identification of diabetic retinopathy stages using texture parameters Acharya, U. Rajendra Ng, Eddie Yin-Kwee Tan, Jen Hong Sree, Subbhuraam Vinitha Ng, Kwan-Hoong School of Mechanical and Aerospace Engineering Diabetes is a condition of increase in the blood sugar level higher than the normal range. Prolonged diabetes damages the small blood vessels in the retina resulting in diabetic retinopathy (DR). DR progresses with time without any noticeable symptoms until the damage has occurred. Hence, it is very beneficial to have the regular cost effective eye screening for the diabetes subjects. This paper documents a system that can be used for automatic mass screenings of diabetic retinopathy. Four classes are identified: normal retina, non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), and macular edema (ME). We used 238 retinal fundus images in our analysis. Five different texture features such as homogeneity, correlation, short run emphasis, long run emphasis, and run percentage were extracted from the digital fundus images. These features were fed into a support vector machine classifier (SVM) for automatic classification. SVM classifier of different kernel functions (linear, radial basis function, polynomial of order 1, 2, and 3) was studied. Receiver operation characteristics (ROC) curves were plotted to select the best classifier. Our proposed system is able to identify the unknown class with an accuracy of 85.2%, and sensitivity, specificity, and area under curve (AUC) of 98.9%, 89.5%, and 0.972 respectively using SVM classifier with polynomial kernel of order 3. We have also proposed a new integrated DR index (IDRI) using different features, which is able to identify the different classes with 100% accuracy. 2013-09-23T07:10:00Z 2019-12-06T20:17:31Z 2013-09-23T07:10:00Z 2019-12-06T20:17:31Z 2011 2011 Journal Article Acharya, U. R., Ng, E. Y. K., Tan, J.-H., Sree, S. V., & Ng, K.-H. (2011). An integrated index for the identification of diabetic retinopathy stages using texture parameters. Journal of medical systems, 36(3), 2011-2020. https://hdl.handle.net/10356/100151 http://hdl.handle.net/10220/13593 10.1007/s10916-011-9663-8 en Journal of medical systems
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
description Diabetes is a condition of increase in the blood sugar level higher than the normal range. Prolonged diabetes damages the small blood vessels in the retina resulting in diabetic retinopathy (DR). DR progresses with time without any noticeable symptoms until the damage has occurred. Hence, it is very beneficial to have the regular cost effective eye screening for the diabetes subjects. This paper documents a system that can be used for automatic mass screenings of diabetic retinopathy. Four classes are identified: normal retina, non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), and macular edema (ME). We used 238 retinal fundus images in our analysis. Five different texture features such as homogeneity, correlation, short run emphasis, long run emphasis, and run percentage were extracted from the digital fundus images. These features were fed into a support vector machine classifier (SVM) for automatic classification. SVM classifier of different kernel functions (linear, radial basis function, polynomial of order 1, 2, and 3) was studied. Receiver operation characteristics (ROC) curves were plotted to select the best classifier. Our proposed system is able to identify the unknown class with an accuracy of 85.2%, and sensitivity, specificity, and area under curve (AUC) of 98.9%, 89.5%, and 0.972 respectively using SVM classifier with polynomial kernel of order 3. We have also proposed a new integrated DR index (IDRI) using different features, which is able to identify the different classes with 100% accuracy.
author2 School of Mechanical and Aerospace Engineering
author_facet School of Mechanical and Aerospace Engineering
Acharya, U. Rajendra
Ng, Eddie Yin-Kwee
Tan, Jen Hong
Sree, Subbhuraam Vinitha
Ng, Kwan-Hoong
format Article
author Acharya, U. Rajendra
Ng, Eddie Yin-Kwee
Tan, Jen Hong
Sree, Subbhuraam Vinitha
Ng, Kwan-Hoong
spellingShingle Acharya, U. Rajendra
Ng, Eddie Yin-Kwee
Tan, Jen Hong
Sree, Subbhuraam Vinitha
Ng, Kwan-Hoong
An integrated index for the identification of diabetic retinopathy stages using texture parameters
author_sort Acharya, U. Rajendra
title An integrated index for the identification of diabetic retinopathy stages using texture parameters
title_short An integrated index for the identification of diabetic retinopathy stages using texture parameters
title_full An integrated index for the identification of diabetic retinopathy stages using texture parameters
title_fullStr An integrated index for the identification of diabetic retinopathy stages using texture parameters
title_full_unstemmed An integrated index for the identification of diabetic retinopathy stages using texture parameters
title_sort integrated index for the identification of diabetic retinopathy stages using texture parameters
publishDate 2013
url https://hdl.handle.net/10356/100151
http://hdl.handle.net/10220/13593
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