IEEE Trans Biomed Eng. 2018 Jun;65(6):1382-1390. doi: 10.1109/TBME.2017.2752701. Epub 2017 Sep 15.
People with diabetes mellitus need annual screening to check for the development of diabetic retinopathy (DR). Tracking small retinal changes due to early diabetic retinopathy lesions in longitudinal fundus image sets is challenging due to intra- and intervisit variability in illumination and image quality, the required high registration accuracy, and the subtle appearance of retinal lesions compared to other retinal features. This paper presents a robust and flexible approach for automated detection of longitudinal retinal changes due to small red lesions by exploiting normalized fundus images that significantly reduce illumination variations and improve the contrast of small retinal features. To detect spatio-temporal retinal changes, the absolute difference between the extremes of the multiscale blobness responses of fundus images from two time points is proposed as a simple and effective blobness measure. DR related changes are then identified based on several intensity and shape features by a support vector machine classifier. The proposed approach was evaluated in the context of a regular diabetic retinopathy screening program involving subjects ranging from healthy (no retinal lesion) to moderate (with clinically relevant retinal lesions) DR levels. Evaluation shows that the system is able to detect retinal changes due to small red lesions with a sensitivity of at an average false positive rate of 1 and 2.5 lesions per eye on small and large fields-of-view of the retina, respectively.
糖尿病患者需要进行年度筛查，以检查是否患有糖尿病视网膜病变 (DR)。由于照明和图像质量的内在和随访变化、所需的高精度配准以及与其他视网膜特征相比视网膜病变的细微表现，对纵向眼底图像集中由于早期糖尿病视网膜病变病变而导致的微小视网膜变化进行跟踪具有挑战性。本文提出了一种稳健且灵活的方法，通过利用归一化眼底图像自动检测由于小红斑引起的纵向视网膜变化，显著减少照明变化并提高小视网膜特征的对比度。为了检测时空视网膜变化，提出了将两个时间点的眼底图像的多尺度斑点响应的极值之间的绝对差异作为一种简单有效的斑点度量。然后通过支持向量机分类器基于几个强度和形状特征来识别与 DR 相关的变化。所提出的方法在涉及从健康（无视网膜病变）到中度（有临床相关视网膜病变）DR 水平的受试者的常规糖尿病视网膜病变筛查计划的背景下进行了评估。评估表明，该系统能够以平均假阳性率为 1 和 2.5 的水平检测由于小的红色病变引起的视网膜变化，分别在小和大的视网膜视场中。
