Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means ClusteringReport as inadecuate




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1

Department of Information Technology, Sirindhorn International Institute of Technology, Thammasat University 131 Moo 5, Tiwanont Road, Bangkadi, Muang, Pathumthani 12000, Thailand

2

Faculty of Computing, Information Systems and Mathematics, Kingston University Penrhyn Road, Kingston upon Thames, Surrey, KT1 2EE, UK





*

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Abstract 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. View Full-Text

Keywords: exudates; diabetic retinopathy; non-dilated retinal images; Fuzzy C-Means clustering exudates; diabetic retinopathy; non-dilated retinal images; Fuzzy C-Means clustering





Author: Akara Sopharak 1,* , Bunyarit Uyyanonvara 1 and Sarah Barman 2

Source: http://mdpi.com/



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