Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering
Exudates are the primary sign of Diabetic Retinopathy. Early detection can potentially reduce the risk of blindness. An automatic method to detect exudates from low-contrast digital images of retinopathy patients with non-dilated pupils using a Fuzzy C-Means (FCM) clustering is proposed. Contrast en...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2009-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/9/3/2148/ |
| _version_ | 1811184464024305664 |
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| author | Akara Sopharak Sarah Barman Bunyarit Uyyanonvara |
| author_facet | Akara Sopharak Sarah Barman Bunyarit Uyyanonvara |
| author_sort | Akara Sopharak |
| collection | DOAJ |
| description | Exudates are the primary sign of Diabetic Retinopathy. Early detection can potentially reduce the risk of blindness. An automatic method to detect exudates from low-contrast digital images of retinopathy patients with non-dilated pupils using a Fuzzy C-Means (FCM) clustering is proposed. Contrast enhancement preprocessing is applied before four features, namely intensity, standard deviation on intensity, hue and a number of edge pixels, are extracted to supply as input parameters to coarse segmentation using FCM clustering method. The first result is then fine-tuned with morphological techniques. The detection results are validated by comparing with expert ophthalmologists’ hand-drawn ground-truths. Sensitivity, specificity, positive predictive value (PPV), positive likelihood ratio (PLR) and accuracy are used to evaluate overall performance. It is found that the proposed method detects exudates successfully with sensitivity, specificity, PPV, PLR and accuracy of 87.28%, 99.24%, 42.77%, 224.26 and 99.11%, respectively. |
| first_indexed | 2024-04-11T13:13:52Z |
| format | Article |
| id | doaj.art-a1872117b32244eebb5db90222e645f5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:13:52Z |
| publishDate | 2009-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a1872117b32244eebb5db90222e645f52022-12-22T04:22:28ZengMDPI AGSensors1424-82202009-03-01932148216110.3390/s90302148Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means ClusteringAkara SopharakSarah BarmanBunyarit UyyanonvaraExudates are the primary sign of Diabetic Retinopathy. Early detection can potentially reduce the risk of blindness. An automatic method to detect exudates from low-contrast digital images of retinopathy patients with non-dilated pupils using a Fuzzy C-Means (FCM) clustering is proposed. Contrast enhancement preprocessing is applied before four features, namely intensity, standard deviation on intensity, hue and a number of edge pixels, are extracted to supply as input parameters to coarse segmentation using FCM clustering method. The first result is then fine-tuned with morphological techniques. The detection results are validated by comparing with expert ophthalmologists’ hand-drawn ground-truths. Sensitivity, specificity, positive predictive value (PPV), positive likelihood ratio (PLR) and accuracy are used to evaluate overall performance. It is found that the proposed method detects exudates successfully with sensitivity, specificity, PPV, PLR and accuracy of 87.28%, 99.24%, 42.77%, 224.26 and 99.11%, respectively.http://www.mdpi.com/1424-8220/9/3/2148/exudatesdiabetic retinopathynon-dilated retinal imagesFuzzy C-Means clustering |
| spellingShingle | Akara Sopharak Sarah Barman Bunyarit Uyyanonvara Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering Sensors exudates diabetic retinopathy non-dilated retinal images Fuzzy C-Means clustering |
| title | Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering |
| title_full | Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering |
| title_fullStr | Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering |
| title_full_unstemmed | Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering |
| title_short | Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering |
| title_sort | automatic exudate detection from non dilated diabetic retinopathy retinal images using fuzzy c means clustering |
| topic | exudates diabetic retinopathy non-dilated retinal images Fuzzy C-Means clustering |
| url | http://www.mdpi.com/1424-8220/9/3/2148/ |
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