Currently, there is an increasing interest for setting up medical systems that can screen a large number of people for sight threatening diseases, such as diabetic retinopathy. This paper presents a method for automated identification of exudate pathologies in retinopathy images based on computational intelligence techniques. The color retinal images are segmented using fuzzy c-means clustering following some preprocessing steps, i.e., color normalization and contrast enhancement. The entire segmented images establish a dataset of regions. To classify these segmented regions into exudates and nonexudates, a set of initial features such as color, size, edge strength, and texture are extracted. A genetic-based algorithm is used to rank the features and identify the subset that gives the best classification results. The selected feature vectors are then classified using a multilayer neural network classifier. The algorithm was implemented using a large image dataset consisting of 300 manually labeled retinal images, and could identify affected retinal images with 96.0% sensitivity while it recognized 94.6% of the normal images, i.e., the specificity. Moreover, the proposed scheme illustrated an accuracy including 93.5% sensitivity and 92.1% predictivity for identification of retinal exudates at the pixel level.

Aim: To identify retinal exudates automatically from colour retinal images. Methods: The colour retinal images were segmented using fuzzy C-means clustering following some key preprocessing steps. To classify the segmented regions into exudates and non-exudates, an artificial neural network classifier was investigated.Results: The proposed system can achieve a diagnostic accuracy with 95.0% sensitivity and 88.9% specificity for the identification of images containing any evidence of retinopathy, where the trade off between sensitivity and specificity was appropriately balanced for this particular problem. Furthermore, it demonstrates 93.0% sensitivity and 94.1% specificity in terms of exudate based classification. Conclusions: This study indicates that automated evaluation of digital retinal images could be used to screen for exudative diabetic retinopathy. Intraretinal fatty (hard) exudates are a visible sign of diabetic retinopathy and a marker for the presence of coexistent retinal oedema. If present in the macular area, they are a major cause of treatable visual loss in the nonproliferative forms of diabetic retinopathy. It would be useful to have an automated method of detecting exudates in digital retinal images produced from diabetic retinopathy screening programmes.Sinthanayothin 1 identified exudates in grey level images based on a recursive region growing technique. The sensitivity and specificity reported were 88.5% and 99.7%; however, these measurements were based on 10610 windows where each window was considered as an exudate or a non-exudate region. The reported sensitivity and specificity only represent an approximate accuracy of exudate recognition, because any particular 10610 window may be only partially affected by exudates. Gardner et al 2 used a neural network (NN) to identify the exudates in grey level images. The authors reported a sensitivity of 93.1%. Again this was the result of classifying whole 20620 regions rather than a pixel level classification. One novelty of our proposed method here is that we locate exudates at pixel resolution rather than estimate for regions. We evaluate the performance of our system applying both lesion based and image based criteria in colour retinal images. MATERIALS AND METHODSWe used 142 colour retinal images obtained from a Canon CR6-45 non-mydriatic retinal camera with a 45˚field of view as our initial image dataset. This consisted of 75 images for training and testing our NN classifier in the exudate based classification stage. The remaining 67 colour images were employed to investigate the diagnostic accuracy of our system for identification of images containing any evidence of retinopathy. The image resolution was 7606570 at 24 bit RGB.Preprocessing Typically, there is wide variation in the colour of the fundus from different patients, related to race and iris colour. The first step is therefore to normalise the retinal images across the set. We selected a particular retinal image as a reference and then used histogram specification 3 to modify the ...

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