Department of Electronics and Communication Engineering, Vimal Jyothi Engineering College, Chemperi Kannur, Kerala, 670632, India.
Department of Electronics and Communication Engineering, Toc H Institute of Science and Technology, Arakkunnam,Ernakulam, Kerala, 682313, India.
J Med Syst. 2019 May 29;43(7):209. doi: 10.1007/s10916-019-1349-7.
It is one of the most vital symptoms of DR (diabetic retinopathy) called hard exudates (HE), which are the leakage of cellular debris and lipoprotein from damaged blood vessels of retina. The vision loss is avoided if the detection of HE in the beginning times. Therefore, a novel method is proposed to detect hard exudates automatically. Previously, for exudate prediction supervised and unsupervised methods have been used. Fault detection of hard exudates, miss classification rate will affect these models because of the characteristics like, similarities with other components in the retinal image and intra variations. For that, the retinal fundus images has been used as input. Then these images are pre-processed with some pre-processing algorithms like image enhancement, equalization of histogram to improve the proposed system performance. Total image data files are divided to training and testing datasets. Features are extracted for training and testing using feature extraction algorithm individually. Then classifier algorithm predicts whether the hard exudate is proliferative or non-proliferative. We obtained accuracy of 99.34% using our proposed methods on public datasets like DIARETDB1 and DRIVE.
它是糖尿病性视网膜病变（DR）的最重要症状之一，称为硬性渗出物（HE），这是视网膜受损血管中细胞碎片和脂蛋白的渗漏。如果在早期发现 HE，可以避免视力丧失。因此，提出了一种新的自动检测硬性渗出物的方法。以前，已经使用了有监督和无监督的方法来预测渗出物。硬性渗出物的故障检测，由于与视网膜图像中的其他成分相似和内部变化等特征，漏报率会影响这些模型。为此，将眼底图像用作输入。然后，使用一些预处理算法对这些图像进行预处理，例如图像增强、直方图均衡化，以提高所提出系统的性能。将总图像数据文件划分为训练和测试数据集。使用特征提取算法分别对训练和测试数据进行特征提取。然后，分类器算法预测硬性渗出物是增生性还是非增生性。我们在 DIARETDB1 和 DRIVE 等公共数据集上使用我们提出的方法获得了 99.34%的准确率。
