Abstract
Hard exudates in retinal images are one of the most prevalent earliest signs of diabetic retinopathy. The accurate identification of hard exudates is of increasing importance in the early detection of diabetic retinopathy. In this paper, we present a novel method to identify hard exudates from digital retinal images. A feature combination based on stationary wavelet transform (SWT) and gray level co-occurrence matrix (GLCM) is used to characterize hard exudates candidates. An optimized support vector machine (SVM) with Gaussian radial basis function is employed as a classifier. A sample dataset consisting of 50 hard exudates candidates is used for identifying hard exudates. With the optimal SVM parameters, the classification accuracy of 84%, sensitivity of 88% and specificity of 80% are obtained.
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