糖尿病视网膜病变(简称“糖网”)渗出物的自动检测对于糖网的早期诊断具有重要意义。针对以往利用数学形态学检测糖网渗出物方法中存在的图像增强效果不佳造成的渗出物细节易漏检以及干扰区域去除不完全造成的正常区域易误检的问题，提出了一种改进的基于数学形态学的糖网渗出物的自动检测方法，主要对眼底图像的预处理和视盘等干扰区域的检测进行了优化。首先预处理阶段在HSV 颜色空间对图像进行亮度校正后引入了多尺度顶帽变换方法进行图像增强，接着采用了一种综合图像边缘信息和亮度信息的新方法定位视盘中心并利用Chan-Vese 水平集模型分割出视盘，又依次提取出干扰渗出物检测的边界和光学器件的反射亮斑，最后用背景估计结合形态学重建的方法检测出渗出物的精确轮廓。经最新公开的e-ophtha EX 数据库测试，得到病灶水平灵敏度91.7%，阳性预测值94.6%；图像水平灵敏度100%，特异性88.6%，准确率95.1%。

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.