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Automatic exudate detection for diabetic retinopathy screening

Akara Sopharak^a,*, Bunyarit Uyyanonvara^a, Sarah Barman^b

 
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.

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* Corresponding author, E-mail: akara@siit.tu.ac.th

Received 8 Sep 2008, Accepted 12 Feb 2009