Wang Su, Tang Hongying Lilian, Al Turk Lutfiah Ismail, Hu Yin, Sanei Saeid, Saleh George Michael, Peto Tunde
IEEE Trans Biomed Eng. 2017 May;64(5):990-1002. doi: 10.1109/TBME.2016.2585344. Epub 2016 Jun 27.
Reliable recognition of microaneurysms (MAs) is an essential task when developing an automated analysis system for diabetic retinopathy (DR) detection. In this study, we propose an integrated approach for automated MA detection with high accuracy.
Candidate objects are first located by applying a dark object filtering process. Their cross-section profiles along multiple directions are processed through singular spectrum analysis. The correlation coefficient between each processed profile and a typical MA profile is measured and used as a scale factor to adjust the shape of the candidate profile. This is to increase the difference in their profiles between true MAs and other non-MA candidates. A set of statistical features of those profiles is then extracted for a K-nearest neighbor classifier.
Experiments show that by applying this process, MAs can be separated well from the retinal background, the most common interfering objects and artifacts.
The results have demonstrated the robustness of the approach when testing on large scale datasets with clinically acceptable sensitivity and specificity.
The approach proposed in the evaluated system has great potential when used in an automated DR screening tool or for large scale eye epidemiology studies.
在开发用于糖尿病视网膜病变（DR）检测的自动分析系统时，可靠识别微动脉瘤（MA）是一项重要任务。在本研究中，我们提出了一种用于高精度自动检测MA的综合方法。
首先通过应用暗物体过滤过程定位候选对象。通过奇异谱分析处理它们沿多个方向的横截面轮廓。测量每个处理后的轮廓与典型MA轮廓之间的相关系数，并将其用作调整候选轮廓形状的比例因子。这是为了增加真实MA与其他非MA候选对象之间轮廓的差异。然后为K近邻分类器提取这些轮廓的一组统计特征。
实验表明，通过应用此过程，可以将MA与视网膜背景、最常见的干扰对象和伪像很好地分离。
结果表明，该方法在具有临床可接受的敏感性和特异性的大规模数据集上进行测试时具有稳健性。
所评估系统中提出的方法在用于自动DR筛查工具或大规模眼部流行病学研究时具有巨大潜力。
