Glaucoma is a chronic disease that threatens eye health and can cause permanent blindness. Since there is no cure for glaucoma, early screening and detection are crucial for the prevention of glaucoma. Therefore, a novel method for automatic glaucoma screening that combines clinical measurement features with image-based features is proposed in this paper. To accurately extract clinical measurement features, an improved UNet++ neural network is proposed to segment the optic disc and optic cup based on region of interest (ROI) simultaneously. Some important clinical measurement features, such as optic cup to disc ratio, are extracted from the segmentation results. Then, the increasing field of view (IFOV) feature model is proposed to fully extract texture features, statistical features, and other hidden image-based features. Next, we select the best feature combination from all the features and use the adaptive synthetic sampling approach to alleviate the uneven distribution of training data. Finally, a gradient boosting decision tree (GBDT) classifier for glaucoma screening is trained. Experimental results based on the ORIGA dataset show that the proposed algorithm achieves excellent glaucoma screening performance with sensitivity of 0.894, accuracy of 0.843, and AUC of 0.901, which is superior to other existing methods.

Graphical abstract

青光眼是威胁眼睛健康并可能导致永久性失明的慢性疾病。由于无法治愈青光眼，因此早期筛查和检测对于预防青光眼至关重要。因此，本文提出了一种新的自动青光眼筛查方法，该方法将临床测量特征与基于图像的特征相结合。为了准确地提取临床测量特征，提出了一种改进的UNet ++神经网络以同时基于感兴趣区域（ROI）分割视盘和视杯。从分割结果中提取了一些重要的临床测量功能，例如视杯与椎间盘的比率。然后，提出了增加视野（IFOV）的特征模型，以完全提取纹理特征，统计特征以及其他基于隐藏图像的特征。下一个，我们从所有特征中选择最佳特征组合，并使用自适应综合采样方法来缓解训练数据的不均匀分布。最后，训练用于青光眼筛查的梯度增强决策树（GBDT）分类器。基于ORIGA数据集的实验结果表明，该算法具有良好的青光眼筛查性能，灵敏度为0.894，准确度为0.843，AUC为0.901，优于其他现有方法。

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