Detecting diabetic retinopathy exudates in fundus images using fuzzy c-means (FCM)
Diabetic Retinopathy (DR) is the main cause of blindness for diabetic patients. As the exudates are the primary sign of DR, therefore early detection and timely treatment can prevent and delay the risk of vision loss. Automatic screening could facilitate the screening process, reduce inspection time...
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An-Najah National University
2020-10-01
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| Series: | مجلة جامعة النجاح للأبحاث العلوم الطبيعية |
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| Online Access: | https://journals.najah.edu/media/journals/full_texts/3_4mBZdHr.pdf |
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| author | Tahreer Dwickat Hadi Hamad |
| author_facet | Tahreer Dwickat Hadi Hamad |
| author_sort | Tahreer Dwickat |
| collection | DOAJ |
| description | Diabetic Retinopathy (DR) is the main cause of blindness for diabetic patients. As the exudates are the primary sign of DR, therefore early detection and timely treatment can prevent and delay the risk of vision loss. Automatic screening could facilitate the screening process, reduce inspection time, and increase accuracy, which is vital in ophthalmic treatment, this development of exudates detection will help doctors in detecting symptoms faster. In this research, we use an automatic method to detect exudates from retinal digital images with non-dilated pupils of retinopathy patients; starting by detecting both the optic disc (OD) and retinal vessels, then probable exudates are defined through morphological techniques, in the last main phase, four features are implemented as input data for the fuzzy C-means (FCM) clustering to define the existing exudates in the fundus images. The overall detection performance is evaluated through measuring sensitivity, specificity, positive predictive value (PPV), positive likelihood ratio (PLR) and accuracy. These measures are done by comparing results to hand-drawn ground truth (GT) done by an expert; which are comparatively analyzed. It is found that the proposed method detects exudates successfully with average values of sensitivity, specificity, PPV, PLR and accuracy of 86.3%, 98.4%, 20.8%, 86.2 and 98.4% respectively on the testing studied database. |
| format | Article |
| id | doaj-art-dbb83d55c2474ed2a8f60226b260341a |
| institution | DOAJ |
| issn | 1727-2114 2311-8865 |
| language | English |
| publishDate | 2020-10-01 |
| publisher | An-Najah National University |
| record_format | Article |
| series | مجلة جامعة النجاح للأبحاث العلوم الطبيعية |
| spelling | doaj-art-dbb83d55c2474ed2a8f60226b260341a2025-08-20T02:56:27ZengAn-Najah National Universityمجلة جامعة النجاح للأبحاث العلوم الطبيعية1727-21142311-88652020-10-01351376410.35552/anujr.a.35.1.1861Detecting diabetic retinopathy exudates in fundus images using fuzzy c-means (FCM)Tahreer Dwickat0Hadi Hamad1MA student: Department of Mathematics, Faculty of Science, An-Najah National University, Nablus, PalestineDepartment of Mathematics, Faculty of Science, An-Najah National University, Nablus, Palestine.Diabetic Retinopathy (DR) is the main cause of blindness for diabetic patients. As the exudates are the primary sign of DR, therefore early detection and timely treatment can prevent and delay the risk of vision loss. Automatic screening could facilitate the screening process, reduce inspection time, and increase accuracy, which is vital in ophthalmic treatment, this development of exudates detection will help doctors in detecting symptoms faster. In this research, we use an automatic method to detect exudates from retinal digital images with non-dilated pupils of retinopathy patients; starting by detecting both the optic disc (OD) and retinal vessels, then probable exudates are defined through morphological techniques, in the last main phase, four features are implemented as input data for the fuzzy C-means (FCM) clustering to define the existing exudates in the fundus images. The overall detection performance is evaluated through measuring sensitivity, specificity, positive predictive value (PPV), positive likelihood ratio (PLR) and accuracy. These measures are done by comparing results to hand-drawn ground truth (GT) done by an expert; which are comparatively analyzed. It is found that the proposed method detects exudates successfully with average values of sensitivity, specificity, PPV, PLR and accuracy of 86.3%, 98.4%, 20.8%, 86.2 and 98.4% respectively on the testing studied database.https://journals.najah.edu/media/journals/full_texts/3_4mBZdHr.pdfdiabetic retinopathyfcm.retinaexudates |
| spellingShingle | Tahreer Dwickat Hadi Hamad Detecting diabetic retinopathy exudates in fundus images using fuzzy c-means (FCM) مجلة جامعة النجاح للأبحاث العلوم الطبيعية diabetic retinopathy fcm. retina exudates |
| title | Detecting diabetic retinopathy exudates in fundus images using fuzzy c-means (FCM) |
| title_full | Detecting diabetic retinopathy exudates in fundus images using fuzzy c-means (FCM) |
| title_fullStr | Detecting diabetic retinopathy exudates in fundus images using fuzzy c-means (FCM) |
| title_full_unstemmed | Detecting diabetic retinopathy exudates in fundus images using fuzzy c-means (FCM) |
| title_short | Detecting diabetic retinopathy exudates in fundus images using fuzzy c-means (FCM) |
| title_sort | detecting diabetic retinopathy exudates in fundus images using fuzzy c means fcm |
| topic | diabetic retinopathy fcm. retina exudates |
| url | https://journals.najah.edu/media/journals/full_texts/3_4mBZdHr.pdf |
| work_keys_str_mv | AT tahreerdwickat detectingdiabeticretinopathyexudatesinfundusimagesusingfuzzycmeansfcm AT hadihamad detectingdiabeticretinopathyexudatesinfundusimagesusingfuzzycmeansfcm |