Abstraction of Exudates in Color Fundus Images
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Diabetic retinopathy is a major cause of blindness. Earliest signs of diabetic retinopathy are damage to blood vessels in the eye and then the formation of lesions in the retina. This project presents a method for the detection of abnormalities in the retina such as the exudates in retinopathy images using computational intelligence techniques. The color retinal images are segmented using fuzzy c-means clustering following color normalization and contrast enhancement. For classification of 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. Using a multilayer neural network classifier, the selected feature vectors are then classified.
KeywordsFuzzy c-means (FCMs) Kmeans algorithm Gabor filters Neural Networks (NNs)
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