为自动检测出眼底图像中的硬性渗出,构建眼底图像的糖尿病视网膜病变自动筛查系统,提出一种基于RBF神经网络和阈值分割的硬性渗出自动检测方法。首先,利用基于最小类内离散度的改进Otsu分割方法对眼底图像绿色通道进行粗分割获取病灶候选区域;然后,利用logistic回归对候选区域的多个特征进行选择;最后,利用候选区域的优化特征集及相应判定结果建立RBF神经网络;此外,提出采用后处理以进一步提高检测精度。利用本文方法对50幅不同颜色、不同亮度的眼底图像进行硬性渗出自动检测,得到图像水平灵敏度100%,特异性90.9%,准确率96.0%;病灶区域水平灵敏度93.9%,阳性预测值95.5%;平均每幅图像处理时间13.6 s。结果表明本文方法稳定可靠,能快速有效地自动检测出眼底图像中的硬性渗出。

To automatically detect hard exudates from fundus images, and to develop an automatic diabetic retinopathy screening system, an automatically detecting approach based on RBF neural network and threshold segmentation was established and studied. First, the green channel of the original fundus image is coarsely segmented by an improved Otsu thresholding based on minimum inner-cluster variance, and candidate regions are obtained. Second, several features of candidate regions are extracted and selected by means of logistic regression. Finally, the RBF neural network is built with the optimal subset of features and judgments of these candidate regions. Furthermore, post-processing is carried out to improve the detection accuracy. The approach is tested on a new set which contained 50 fundus images with variable color and brightness. With an image-based criterion, sensitivity of 100%, specificity of 90.9%, and accuracy of 96.0% are achieved. Average sensitivity of 93.9% and average positive predict value of 95.5% are also achieved with a lesion-based criterion. Furthermore, the average time cost in processing an image is 13.6 s. Results suggest that the approach is stable and reliable, and can fast and effectively detect hard exudates from fundus images.