Faculty of Engineering, Department of Electrical Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
J Med Syst. 2011 Feb;35(1):17-24. doi: 10.1007/s10916-009-9337-y. Epub 2009 Jul 4.
The increasing number of diabetic retinopathy (DR) cases world wide demands the development of an automated decision support system for quick and cost-effective screening of DR. We present an automatic screening system for detecting the early stage of DR, which is known as non-proliferative diabetic retinopathy (NPDR). The proposed system involves processing of fundus images for extraction of abnormal signs, such as hard exudates, cotton wool spots, and large plaque of hard exudates. A rule based classifier is used for classifying the DR into two classes, namely, normal and abnormal. The abnormal NPDR is further classified into three levels, namely, mild, moderate, and severe. To evaluate the performance of the proposed decision support framework, the algorithms have been tested on the images of STARE database. The results obtained from this study show that the proposed system can detect the bright lesions with an average accuracy of about 97%. The study further shows promising results in classifying the bright lesions correctly according to NPDR severity levels.
全球范围内糖尿病性视网膜病变（DR）病例的不断增加，要求开发一种自动化决策支持系统，以便快速、经济有效地筛查 DR。我们提出了一种用于检测 DR 早期阶段（即非增殖性糖尿病性视网膜病变（NPDR））的自动筛查系统。该系统涉及对眼底图像进行处理，以提取异常迹象，如硬性渗出物、棉絮斑和硬性渗出斑块。基于规则的分类器用于将 DR 分为正常和异常两类。异常 NPDR 进一步分为轻度、中度和重度三个级别。为了评估所提出的决策支持框架的性能，已经在 STARE 数据库的图像上测试了算法。该研究的结果表明，该系统可以检测到明亮的病变，平均准确率约为 97%。该研究还表明，根据 NPDR 严重程度正确分类明亮病变具有很有前景的结果。
