Welikala R A, Fraz M M, Foster P J, Whincup P H, Rudnicka A R, Owen C G, Strachan D P, Barman S A
School of Computing and Information Systems, Kingston University, Surrey KT1 2EE, United Kingdom.
School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad 44000, Pakistan.
Comput Biol Med. 2016 Apr 1;71:67-76. doi: 10.1016/j.compbiomed.2016.01.027. Epub 2016 Feb 6.
Morphological changes in the retinal vascular network are associated with future risk of many systemic and vascular diseases. However, uncertainty over the presence and nature of some of these associations exists. Analysis of data from large population based studies will help to resolve these uncertainties. The QUARTZ (QUantitative Analysis of Retinal vessel Topology and siZe) retinal image analysis system allows automated processing of large numbers of retinal images. However, an image quality assessment module is needed to achieve full automation. In this paper, we propose such an algorithm, which uses the segmented vessel map to determine the suitability of retinal images for use in the creation of vessel morphometric data suitable for epidemiological studies. This includes an effective 3-dimensional feature set and support vector machine classification. A random subset of 800 retinal images from UK Biobank (a large prospective study of 500,000 middle aged adults; where 68,151 underwent retinal imaging) was used to examine the performance of the image quality algorithm. The algorithm achieved a sensitivity of 95.33% and a specificity of 91.13% for the detection of inadequate images. The strong performance of this image quality algorithm will make rapid automated analysis of vascular morphometry feasible on the entire UK Biobank dataset (and other large retinal datasets), with minimal operator involvement, and at low cost.
视网膜血管网络的形态学变化与许多全身性和血管性疾病的未来风险相关。然而，其中一些关联的存在和性质仍存在不确定性。对基于大样本人群研究的数据进行分析将有助于解决这些不确定性。QUARTZ（视网膜血管拓扑和大小定量分析）视网膜图像分析系统允许对大量视网膜图像进行自动化处理。然而，需要一个图像质量评估模块来实现完全自动化。在本文中，我们提出了这样一种算法，该算法使用分割后的血管图来确定视网膜图像是否适合用于创建适用于流行病学研究的血管形态计量数据。这包括一个有效的三维特征集和支持向量机分类。我们使用来自英国生物银行（一项对50万名中年成年人的大型前瞻性研究，其中68151人接受了视网膜成像）的800张视网膜图像的随机子集来检验图像质量算法的性能。该算法在检测不合格图像方面的灵敏度为95.33%，特异性为91.13%。这种图像质量算法的强大性能将使在整个英国生物银行数据集（以及其他大型视网膜数据集）上以最少的操作员参与和低成本对血管形态计量进行快速自动化分析成为可能。
