Diabetic Retinopathy is a severe and widely spread eye disease which can be regarded as manifestation of diabetes on the retina. Screening to detect retinopathy disease can lead to successful treatments in preventing visual loss. Intraretinal fatty (hard) exudates are a visible sign of diabetic retinopathy and also a marker for the presence of co-existent retinal oedema. Detecting retinal exudate lesions in a large number of images generated by screening programmes, is very expensive in professional time and opens to human error. In this thesis we explore the benefits of developing an automated decision support system for the purpose of detecting and classifying exudate pathologies of diabetic retinopathy. The retinal images are automatically analysed in terms of pixel resolution and image-based diagnostic accuracies and an assessment of the level of retinopathy is derived. A pixel-level exudate recognition approach is first attempted to discriminate the exudates from other retinal anatomical-pathological structures and artifacts. To estimate the exudate and non-exudate probability density distributions, K nearest neighbour, Gaussian quadratic and Gaussian mixture model classifiers are investigated. The preliminary pixel-level exudate recognition analysis has been used to support the fact that the development of a reliable and accurate exudate identification system is feasible. We explore another method, i.e. region-level exudate recognition to identify the retinal exudates based on an object recognition scheme. This includes colour image segmentation and region-level classification based on neural network and support vector machine classifier models. The location of the optic disc is of critical importance in retinal image analysis and is required as a prerequisite stage of exudate detection. Therefore, we also address optic disc localisation and segmentation both to improve the overall diagnostic accuracy by masking the false positive optic disc regions from the other sought exudates and to measure its boundary precisely. We develop a method based on colour mathematical morphology and active contours to accurately localise the optic disc region.