Abstract

Bright lesions, including exudates and cotton wool spots, are the main symptoms in diabetic retinopathy. Early detection and classification of such evidence is essential for an effective treatment. A three-stage approach is applied to detect and classify bright lesions. After a local contrast enhancement preprocessing stage, two-step improved fuzzy C-means is applied in Luv color space to segment candidate bright-lesion areas. The results are shown to be effective in dealing with the inhomogeneous illumination of the fundus images while reducing the influence of noise. Finally, a hierarchical support vector machine (SVM) classification structure is successfully applied to classify bright non-lesion areas, exudates and cotton wool spots.

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