Detection and classification of bright lesions in color fundus images

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.