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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.

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ABSTRACT:Exudates are one of the primary signs of diabetic retinopathy which is a main cause of blindness that could be prevented with an early screening process. Pupil dilation is required in the normal screening process but this affects patients' vision. Automatic computerized screening could facilitate the screening process, reduce inspection time, and increase accuracy. In this paper we propose 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 technique. Preprocessing of contrast enhancement was applied in order to enhance the quality of the input image before four features, namely, intensity, standard deviation on intensity, hue, and number of edge pixels, were selected to supply to the FCM method. The number of required clusters was optimally selected from a quantitative experiment where it was varied from two to eight clusters. The number of cluster optimization was based on sensitivity and specificity which were calculated by comparison of the detected results and hand-drawn ground truths from expert ophthalmologists. The positive predictive value and positive likelihood ratio were also used to evaluate the overall performance of this method. From the result of the subtracted cluster with the number of clusters equalling 2, it was found that the proposed method detected exudates with 92.18% sensitivity and 91.52% sensitivity.