Yin Xiao-Xia, Irshad Samra, Zhang Yanchun
1Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006 China.
2Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, Australia.
Health Inf Sci Syst. 2019 Nov 8;7(1):26. doi: 10.1007/s13755-019-0090-4. eCollection 2019 Dec.
The morphological changes in retinal blood vessels indicate cardiovascular diseases and consequently those diseases lead to ocular complications such as Hypertensive Retinopathy. One of the significant clinical findings related to this ocular abnormality is alteration of width of vessel. The classification of retinal vessels into arteries and veins in eye fundus images is a relevant task for the automatic assessment of vascular changes. This paper presents an important approach to solve this problem by means of feature ranking strategies and multiple classifiers decision-combination scheme that is specifically adapted for artery/vein classification. For this, three databases are used with a local dataset of 44 images and two publically available databases, INSPIRE-AVR containing 40 images and VICAVR containing 58 images. The local database also contains images with pathologically diseased structures. The performance of the proposed system is assessed by comparing the experimental results with the gold standard estimations as well as with the results of previous methodologies, achieving promising classification performance, with an over all accuracy of 90.45%, 93.90% and 87.82%, in retinal blood vessel separation for Local, INSPIRE-AVR and VICAVR dataset, respectively.
视网膜血管的形态变化表明存在心血管疾病，因此这些疾病会导致诸如高血压性视网膜病变等眼部并发症。与这种眼部异常相关的一个重要临床发现是血管宽度的改变。在眼底图像中将视网膜血管分为动脉和静脉是自动评估血管变化的一项相关任务。本文提出了一种重要方法，通过特征排序策略和专门适用于动脉/静脉分类的多分类器决策组合方案来解决这个问题。为此，使用了三个数据库，一个包含44幅图像的本地数据集以及两个公开可用的数据库，即包含40幅图像的INSPIRE - AVR和包含58幅图像的VICAVR。本地数据库还包含有病理病变结构的图像。通过将实验结果与金标准估计值以及先前方法的结果进行比较，来评估所提出系统的性能，在局部、INSPIRE - AVR和VICAVR数据集的视网膜血管分离中分别实现了90.45%、93.90%和87.82%的总体准确率，取得了有前景的分类性能。
