Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering

Sensors

Akara Sopharak¹  Bunyarit Uyyanonvara¹ 

[1] Department of Information Technology, Sirindhorn International Institute of Technology, Thammasat University 131 Moo 5, Tiwanont Road, Bangkadi, Muang, Pathumthani 12000, Thailand; E-mails:

关键词: Exudates;    diabetic retinopathy;    non-dilated retinal images;    Fuzzy C-Means clustering;   

DOI  :  10.3390/s90302148

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2009 by the authors; licensee MDPI, Basel, Switzerland