Diabetic retinopathy is a chronic disorder which is considered as a major source of vision loss in patients suffering from diabetes. It is characterized by the destructive of blood vessels that nourish the retina. However, early detection of such disorder through regular diagnosis, vision loss can be avoided. In order to reduce the diagnosis cost and enhance the automated analysis, modern image processing tools are used to detect the existence of disorders in the retinal images acquired during the initial process of screenings. This paper presents a methodology for the extraction of exudates within blood vessels from fundus images using Fuzzy c-Means (FCM) clustering algorithm. Matched filter was applied for vessel extraction with the help of adaptive histogram equalization, thresholding method and segmenting method, which incorporates spatial neighborhood information into the FCM clustering algorithm. A standard diabetic retinopathy database was used in this study to test the proposed algorithm. This methodology showed improved sensitivity and accuracy of the segmented result. The proposed method seems to be promising as it can also detect the very small areas of exudates. Such an image processing technique can reduce the work of ophthalmologists and help in patient screening, treatment and clinical studies. © 2015, Institute of Integrative Omics and Applied Biotechnology. All rights reserved.