IEEE J Biomed Health Inform. 2018 Jan;22(1):224-234. doi: 10.1109/JBHI.2017.2723678. Epub 2017 Jul 5.
Automated optic disk (OD) detection plays an important role in developing a computer aided system for eye diseases. In this paper, we propose an algorithm for the OD detection based on structured learning. A classifier model is trained based on structured learning. Then, we use the model to achieve the edge map of OD. Thresholding is performed on the edge map, thus a binary image of the OD is obtained. Finally, circle Hough transform is carried out to approximate the boundary of OD by a circle. The proposed algorithm has been evaluated on three public datasets and obtained promising results. The results (an area overlap and Dices coefficients of 0.8605 and 0.9181, respectively, an accuracy of 0.9777, and a true positive and false positive fraction of 0.9183 and 0.0102) show that the proposed method is very competitive with the state-of-the-art methods and is a reliable tool for the segmentation of OD.
自动视盘（OD）检测在开发用于眼部疾病的计算机辅助系统中起着重要作用。在本文中，我们提出了一种基于结构学习的 OD 检测算法。基于结构学习训练分类器模型。然后，我们使用该模型来实现 OD 的边缘图。在边缘图上进行阈值处理，从而得到 OD 的二值图像。最后，通过圆 Hough 变换对 OD 的边界进行近似。该算法已在三个公共数据集上进行了评估，得到了有希望的结果。结果（面积重叠和 Dice 系数分别为 0.8605 和 0.9181，准确率为 0.9777，真阳性和假阳性分数分别为 0.9183 和 0.0102）表明，该方法与现有方法相比具有很强的竞争力，是 OD 分割的可靠工具。
