The automated detection of diabetic retinopathy exudates has great importance for early diagnosis of diabetic retinopathy. Aiming to reduce the residual error caused by ineffective image enhancement and the false detection caused by incomplete removal of interference regions existing in common morphology-based exudates detection methods, an automated method based on mathematical morphology is proposed, which mainly improves the preprocessing of fundus images and the detection of interference regions like optic disc. In the image preprocessing step, the proposed method corrects the brightness of the image in the HSV color space, and then adopts multi-scale top-hat transform to enhance the image. Afterwards, a novel method is used to localize the center of optic disc according to the edge and brightness characteristics of image, and then the optic disc region is segmented by Chan-Vese level set model. Furthermore, other interference regions including bright border and optical artifacts reflection are detected and removed. Finally, the exudates are precisely segmented by background estimation and morphological reconstruction. From the testing results on the new public dataset of e-ophtha EX, the proposed method achieves sensitivity of 91.7% , specificity of 94.6% on the exudate level and sensitivity of 100%, specificity of 88.6% and accuracy of 95.1% on the image level.