Roychowdhury Sohini, Koozekanani Dara D, Parhi Keshab K
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:1300-1303. doi: 10.1109/EMBC.2016.7590945.
Neovascularization is the primary manifestation of proliferative diabetic retinopathy (PDR) that can lead to acquired blindness. This paper presents a novel method that classifies neovascularizations in the 1-optic disc (OD) diameter region (NVD) and elsewhere (NVE) separately to achieve low false positive rates of neovascularization classification. First, the OD region and blood vessels are extracted. Next, the major blood vessel segments in the 1-OD diameter region are classified for NVD, and minor blood vessel segments elsewhere are classified for NVE. For NVD and NVE classifications, optimal region-based feature sets of 10 and 6 features, respectively, are used. The proposed method achieves classification sensitivity, specificity and accuracy for NVD and NVE of 74%, 98.2%, 87.6%, and 61%, 97.5%, 92.1%, respectively. Also, the proposed method achieves 86.4% sensitivity and 76% specificity for screening images with PDR from public and local data sets. Thus, the proposed NVD and NVE detection methods can play a key role in automated screening and prioritization of patients with diabetic retinopathy.
新生血管形成是增殖性糖尿病视网膜病变（PDR）的主要表现，可导致后天性失明。本文提出了一种新方法，分别对视盘（OD）直径区域（NVD）和其他部位（NVE）的新生血管进行分类，以实现较低的新生血管分类假阳性率。首先，提取OD区域和血管。接下来，对OD直径区域的主要血管段进行NVD分类，对其他部位的次要血管段进行NVE分类。对于NVD和NVE分类，分别使用了最优的基于区域的包含10个和6个特征的特征集。所提出的方法对NVD和NVE的分类敏感性、特异性和准确性分别达到74%、98.2%、87.6%以及61%、97.5%、92.1%。此外，对于从公共和本地数据集中筛选患有PDR的图像，所提出的方法实现了86.4%的敏感性和76%的特异性。因此，所提出的NVD和NVE检测方法在糖尿病视网膜病变患者的自动筛查和优先级排序中可以发挥关键作用。
