Harangi B, Lazar I, Hajdu A
University of Debrecen, Faculty of Informatics, Debrecen, 4010 POB. 12, Hungary.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:5951-4. doi: 10.1109/EMBC.2012.6347349.
Diabetic retinopathy is one the most common cause of blindness in the world. Exudates are among the early signs of this disease, so its proper detection is a very important task to prevent consequent effects. In this paper, we propose a novel approach for exudate detection. First, we identify possible regions containing exudates using grayscale morphology. Then, we apply an active contour based method to minimize the Chan-Vese energy to extract accurate borders of the candidates. To remove those false candidates that have sufficient strong borders to pass the active contour method we use a regionwise classifier. Hence, we extract several shape features for each candidate and let a boosted Naïve Bayes classifier eliminate the false candidates. We considered the publicly available DiaretDB1 color fundus image set for testing, where the proposed method outperformed several state-of-the-art exudate detectors.
糖尿病性视网膜病变是全球最常见的致盲原因之一。渗出物是该疾病的早期症状之一，因此对其进行正确检测是预防后续影响的一项非常重要的任务。在本文中，我们提出了一种用于渗出物检测的新方法。首先，我们使用灰度形态学识别可能包含渗出物的区域。然后，我们应用基于主动轮廓的方法来最小化Chan-Vese能量，以提取候选区域的精确边界。为了去除那些具有足够强边界以通过主动轮廓方法的假候选区域，我们使用区域分类器。因此，我们为每个候选区域提取几个形状特征，并让一个增强的朴素贝叶斯分类器消除假候选区域。我们考虑使用公开可用的DiaretDB1彩色眼底图像集进行测试，所提出的方法在该测试中优于几种现有的渗出物检测器。
