Karnowski T P, Aykac D, Chaum E, Giancardo L, Li Y, Tobin K W, Abramoff M D
Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:6205-9. doi: 10.1109/IEMBS.2009.5334626.
The projected increase in diabetes in the United States and worldwide has created a need for broad-based, inexpensive screening for diabetic retinopathy (DR), an eye disease which can lead to vision impairment. A telemedicine network with retina cameras and automated quality control, physiological feature location, and lesion / anomaly detection is a low-cost way of achieving broad-based screening. In this work we report on the effect of quality estimation on an optic nerve (ON) detection method with a confidence metric. We report on an improvement of the method using a data set from an ophthalmologist practice then show the results of the method as a function of image quality on a set of images from an on-line telemedicine network collected in Spring 2009 and another broad-based screening program. We show that the fusion method, combined with quality estimation processing, can improve detection performance and also provide a method for utilizing a physician-in-the-loop for images that may exceed the capabilities of automated processing.
美国及全球预计糖尿病患者人数的增加，使得对糖尿病视网膜病变（DR）进行广泛且低成本筛查的需求应运而生，糖尿病视网膜病变是一种可导致视力损害的眼部疾病。配备视网膜相机以及具备自动质量控制、生理特征定位和病变/异常检测功能的远程医疗网络，是实现广泛筛查的低成本方式。在这项工作中，我们报告了质量评估对一种带有置信度度量的视神经（ON）检测方法的影响。我们报告了使用来自眼科医生诊所的数据集对该方法的改进，然后展示了该方法在2009年春季收集的一组来自在线远程医疗网络的图像以及另一个广泛筛查项目的图像上，作为图像质量函数的结果。我们表明，融合方法与质量评估处理相结合，可以提高检测性能，还能提供一种方法，以便在自动处理能力可能不足的图像上引入医生参与。
