## 1 Introduction  
Diabetic retinopathy eye diseases are the main cause of vision loss and their prevalence is set to continue rising [1]. The screening of diabetic patients for the development of diabetic retinopathy can potentially reduce the risk of blindness in these patients [2-6]. Early detection enables laser therapy to be performed to prevent or delay visual loss and may be used to encourage improvement in diabetic control. Current methods of detection and assessment of diabetic retinopathy are manual, expensive and require trained ophthalmologists. Exudates are one of the primary signs of diabetic retinopathy [7, 8]. Automatic exudates detection would be helpful for diabetic retinopathy screening process. [PERSON] _et al_. proposed an automatic detection of diabetic retinopathy using an artificial neural network. The exudates are identified from grey level images and the fundus image is analyzed using a back propagation neural network. The classification of a 20x20 region is used rather than a pixel-level classification [9]. [PERSON] _et al_. reported the result of an automated detection of diabetic retinopathy on digital fundus images by a Recursive Region Growing Segmentation (RRGS) algorithm on a 10x10 window [10]. In the preprocessing step, adaptive, local, contrast enhancement is applied. The optic disc, blood vessels and fovea detection are also localized [6]. [PERSON] _et al_. used color features on a Bayesian statistical classifier to classify each pixel into lesion or non-lesion classes [11]. [PERSON] _et al_. have applied a thresholding technique based on the selection of regions to detect exudates. A patch of size 256 x 192 pixels is selected over the area of interest. Global thresholding is used to detect the large exudates, while local thresholding is used to detect the lower intensity exudates [12]. [PERSON] _et al_. proposed an exudate extraction technique by using a combination of region growing and edge detection techniques. The optic disc is also detected by principal component analysis (PCA). The shape of the optic disc is detected using a modified active shape model [13]. [PERSON] _et al_. combined color and sharp edge features to detect the exudates. The yellowish objects are detected first; the objects in the image with sharp edges are then detected using [PERSON]'s mask and different rotations of it on the green component. The combination of results of yellowish objects with sharp edges is used to determine the exudates [5]. [PERSON] _et al_. presented a domain knowledge based approach to detect exudates. A median filter is used to compute an intensity difference map. Dynamic clustering is then used to determine lesion clusters. Finally domain knowledge is applied to identify true exudates [2]. [PERSON] _et al_. detected the candidate exudates region by using a combination of RRGS and adaptive intensity thresholding [14]. [PERSON] _et al_. used the minimum distance discriminant to detect the exudates. The spectrum feature center of exudates and background are computed and then the distance from each pixel to class center is calculated. The pixel is classified as exudate if it falls within the minimum distance [15]. [PERSON] _et al_. used a median filter to remove noise. Bright lesions and dark lesions are separated by thresholding. A region growing algorithm is used to locate exudates. Bayesian, Mahalanobis and K-Nearest Neighbor classifier were tested. From these experiments, the Mahalanobis classifier was shown to yield the best results [16]. [PERSON] _et al_. detected exudates using grey level variation and contours determined by means of morphological reconstruction techniques [17]. [PERSON] _et al_. have proposed a machine learning-based to detect exudates [18]. The Fuzzy C-Means (FCM) clustering is a well-known clustering technique for image segmentation. It was developed by [PERSON] [19] and improved by [PERSON] [20]. It has also been used in retinal image segmentation [3, 21-24]. [PERSON] _et al_. used color normalization and a local contrastenhancement in a pre-processing step.