Tobin Kenneth W, Chaum Edward, Govindasamy V Priya, Karnowski Thomas P
Image Science and Machine Vision Group, Oak Ridge National Laboratory, Oak Ridge, TN 37831-6010, USA.
IEEE Trans Med Imaging. 2007 Dec;26(12):1729-39. doi: 10.1109/tmi.2007.902801.
The widespread availability of electronic imaging devices throughout the medical community is leading to a growing body of research on image processing and analysis to diagnose retinal disease such as diabetic retinopathy (DR). Productive computer-based screening of large, at-risk populations at low cost requires robust, automated image analysis. In this paper we present results for the automatic detection of the optic nerve and localization of the macula using digital red-free fundus photography. Our method relies on the accurate segmentation of the vasculature of the retina followed by the determination of spatial features describing the density, average thickness, and average orientation of the vasculature in relation to the position of the optic nerve. Localization of the macula follows using knowledge of the optic nerve location to detect the horizontal raphe of the retina using a geometric model of the vasculature. We report 90.4% detection performance for the optic nerve and 92.5% localization performance for the macula for red-free fundus images representing a population of 345 images corresponding to 269 patients with 18 different pathologies associated with DR and other common retinal diseases such as age-related macular degeneration.
电子成像设备在整个医学界的广泛应用，正促使关于图像处理和分析以诊断视网膜疾病（如糖尿病性视网膜病变（DR））的研究日益增多。以低成本对大量高危人群进行高效的基于计算机的筛查，需要强大的自动图像分析技术。在本文中，我们展示了使用数字无赤眼底摄影自动检测视神经和黄斑定位的结果。我们的方法依赖于对视网膜血管系统的准确分割，然后确定描述血管系统相对于视神经位置的密度、平均厚度和平均方向的空间特征。利用视神经位置的知识，通过血管系统的几何模型来检测视网膜的水平中缝，从而实现黄斑的定位。对于代表345幅图像的无赤眼底图像，我们报告了视神经检测性能为90.4%，黄斑定位性能为92.5%，这些图像对应于269名患有与DR及其他常见视网膜疾病（如年龄相关性黄斑变性）相关的18种不同病变的患者。
