Oloumi Faraz, Rangayyan Rangaraj M, Ells Anna L
University of Calgary , Department of Electrical and Computer Engineering, Schulich School of Engineering, 2500 University Drive N.W., Calgary, AB T2N 1N4, Canada.
University of Calgary, Department of Electrical and Computer Engineering, Schulich School of Engineering, 2500 University Drive N.W., Calgary, AB T2N 1N4, Canada; University of Calgary, Division of Ophthalmology, Department of Surgery, Cumming School of Medicine, 2500 University Drive N.W., Calgary, AB T2N 1N4, Canada.
J Med Imaging (Bellingham). 2016 Oct;3(4):044505. doi: 10.1117/1.JMI.3.4.044505. Epub 2016 Dec 15.
Retinopathy of prematurity (ROP), a disorder of the retina occurring in preterm infants, is the leading cause of preventable childhood blindness. An active phase of ROP that requires treatment is associated with the presence of plus disease, which is diagnosed clinically in a qualitative manner by visual assessment of the existence of a certain level of increase in the thickness and tortuosity of retinal vessels. The present study performs computer-aided diagnosis (CAD) of plus disease via quantitative measurement of tortuosity in retinal fundus images of preterm infants. Digital image processing techniques were developed for the detection of retinal vessels and measurement of their tortuosity. The total lengths of abnormally tortuous vessels in each quadrant and the entire image were then computed. A minimum-length diagnostic-decision-making criterion was developed to assess the diagnostic sensitivity and specificity of the values obtained. The area ([Formula: see text]) under the receiver operating characteristic curve was used to assess the overall diagnostic accuracy of the methods. Using a set of 19 retinal fundus images of preterm infants with plus disease and 91 without plus disease, the proposed methods provided an overall diagnostic accuracy of [Formula: see text]. Using the total length of all abnormally tortuous vessel segments in an image, our techniques are capable of CAD of plus disease with high accuracy without the need for manual selection of vessels to analyze. The proposed methods may be used in a clinical or teleophthalmological setting.
早产儿视网膜病变（ROP）是一种发生于早产儿的视网膜疾病，是儿童可预防性失明的主要原因。需要治疗的ROP活动期与附加病变的存在相关，附加病变通过视觉评估视网膜血管厚度和迂曲度增加到一定程度在临床上进行定性诊断。本研究通过对早产儿眼底图像中血管迂曲度进行定量测量，对附加病变进行计算机辅助诊断（CAD）。开发了数字图像处理技术用于检测视网膜血管并测量其迂曲度。然后计算每个象限及整个图像中异常迂曲血管的总长度。制定了一个最小长度诊断决策标准来评估所获值的诊断敏感性和特异性。利用受试者工作特征曲线下的面积（[公式：见原文]）来评估这些方法的总体诊断准确性。使用一组19张患有附加病变的早产儿眼底图像和91张无附加病变的图像，所提出的方法提供了[公式：见原文]的总体诊断准确性。利用图像中所有异常迂曲血管段的总长度，我们的技术能够高精度地对附加病变进行CAD，而无需手动选择血管进行分析。所提出的方法可用于临床或远程眼科环境。
