Algorithms for the automated detection of diabetic retinopathy using digital fundus images : a review
Diabetes is a chronic end organ disease that occurs when the pancreas does not secrete enough insulin or the body is unable to process it properly. Over time, diabetes affects the circulatory system, including that of the retina. Diabetic retinopathy is a medical condition where the retina is damage...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2013
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| Online Access: | https://hdl.handle.net/10356/100231 http://hdl.handle.net/10220/13595 |
| _version_ | 1811696008542814208 |
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| author | Faust, Oliver Acharya, U. Rajendra Ng, Eddie Yin-Kwee Ng, Kwan-Hoong Suri, Jasjit S. |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Faust, Oliver Acharya, U. Rajendra Ng, Eddie Yin-Kwee Ng, Kwan-Hoong Suri, Jasjit S. |
| author_sort | Faust, Oliver |
| collection | NTU |
| description | Diabetes is a chronic end organ disease that occurs when the pancreas does not secrete enough insulin or the body is unable to process it properly. Over time, diabetes affects the circulatory system, including that of the retina. Diabetic retinopathy is a medical condition where the retina is damaged because fluid leaks from blood vessels into the retina. Ophthalmologists recognize diabetic retinopathy based on features, such as blood vessel area, exudes, hemorrhages, microaneurysms and texture. In this paper we review algorithms used for the extraction of these features from digital fundus images. Furthermore, we discuss systems that use these features to classify individual fundus images. The classifications efficiency of different DR systems is discussed. Most of the reported systems are highly optimized with respect to the analyzed fundus images, therefore a generalization of individual results is difficult. However, this review shows that the classification results improved has improved recently, and it is getting closer to the classification capabilities of human ophthalmologists. |
| first_indexed | 2024-10-01T07:32:32Z |
| format | Journal Article |
| id | ntu-10356/100231 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:32:32Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/1002312020-03-07T13:22:16Z Algorithms for the automated detection of diabetic retinopathy using digital fundus images : a review Faust, Oliver Acharya, U. Rajendra Ng, Eddie Yin-Kwee Ng, Kwan-Hoong Suri, Jasjit S. School of Mechanical and Aerospace Engineering Diabetes is a chronic end organ disease that occurs when the pancreas does not secrete enough insulin or the body is unable to process it properly. Over time, diabetes affects the circulatory system, including that of the retina. Diabetic retinopathy is a medical condition where the retina is damaged because fluid leaks from blood vessels into the retina. Ophthalmologists recognize diabetic retinopathy based on features, such as blood vessel area, exudes, hemorrhages, microaneurysms and texture. In this paper we review algorithms used for the extraction of these features from digital fundus images. Furthermore, we discuss systems that use these features to classify individual fundus images. The classifications efficiency of different DR systems is discussed. Most of the reported systems are highly optimized with respect to the analyzed fundus images, therefore a generalization of individual results is difficult. However, this review shows that the classification results improved has improved recently, and it is getting closer to the classification capabilities of human ophthalmologists. 2013-09-23T07:20:21Z 2019-12-06T20:18:57Z 2013-09-23T07:20:21Z 2019-12-06T20:18:57Z 2010 2010 Journal Article Faust, O., Acharya, U. R., Ng, E. Y. K., Ng, K.-H., & Suri, J. S. (2010). Algorithms for the Automated Detection of Diabetic Retinopathy Using Digital Fundus Images: A Review. Journal of Medical Systems, 36(1), 145-157. https://hdl.handle.net/10356/100231 http://hdl.handle.net/10220/13595 10.1007/s10916-010-9454-7 en Journal of medical systems |
| spellingShingle | Faust, Oliver Acharya, U. Rajendra Ng, Eddie Yin-Kwee Ng, Kwan-Hoong Suri, Jasjit S. Algorithms for the automated detection of diabetic retinopathy using digital fundus images : a review |
| title | Algorithms for the automated detection of diabetic retinopathy using digital fundus images : a review |
| title_full | Algorithms for the automated detection of diabetic retinopathy using digital fundus images : a review |
| title_fullStr | Algorithms for the automated detection of diabetic retinopathy using digital fundus images : a review |
| title_full_unstemmed | Algorithms for the automated detection of diabetic retinopathy using digital fundus images : a review |
| title_short | Algorithms for the automated detection of diabetic retinopathy using digital fundus images : a review |
| title_sort | algorithms for the automated detection of diabetic retinopathy using digital fundus images a review |
| url | https://hdl.handle.net/10356/100231 http://hdl.handle.net/10220/13595 |
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