Diabetes cause’s metabolic and physiological abnormalities in the retina and the changes suggest a role for inflammation in the development of diabetic retinopathy. Abnormal blood vessels can form in the back of the eye of a person with diabetes. These new blood vessels are weaker and prone to breaking and causing hemorrhage (HEs). Diabetic retinopathy (DR) accounts for 31.5–54% of all cases of vitreous hemorrhage in adults in the world. Therefore, detection of HEs is still a challenging factor task for computer-aided diagnostics of DR. Many researchers have developed advanced algorithms of hemorrhages detection using fundus images. In this paper, a robust and computationally efficient approach for HEs with different shape and size detection and classification is presented. First, brightness correction and contrast enhancement are applied to fundus images. Second, candidate hemorrhages are extracted by using an unsupervised classification algorithm. Third, an approach based on mathematical morphology is carried out for vascular network and macula segmentation. Finally, a total of 13 HEs features are considered in this study and selected for classification. The proposed method is evaluated on 419 fundus images of DIARETDB0, DIARETDB1 and MESSIDOR databases. Experimental results show that overall average sensitivity, specificity, predictive value and accuracy for hemorrhage in lesion level are 98.90%, 99.66%, 97.63% and 99.56%, respectively. The results show that the proposed method outperforms other state-of-the-art methods in detection of hemorrhages. These results indicate that this new method may improve the performance of diagnosis of DR system.

中文翻译：

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用于辅助诊断糖尿病视网膜病变的计算机混合系统出血 (HES) 检测

糖尿病引起视网膜的代谢和生理异常，这些变化表明炎症在糖尿病视网膜病变的发展中起作用。糖尿病患者的眼后部会形成异常血管。这些新血管较弱，容易破裂并导致出血 (HE)。糖尿病性视网膜病变 (DR) 占全世界所有成人玻璃体出血病例的 31.5-54%。因此，对于 DR 的计算机辅助诊断，HE 的检测仍然是一项具有挑战性的任务。许多研究人员开发了使用眼底图像检测出血的先进算法。在本文中，提出了一种用于具有不同形状和尺寸的 HEs 检测和分类的稳健且计算效率高的方法。第一的，亮度校正和对比度增强应用于眼底图像。其次，使用无监督分类算法提取候选出血。第三，基于数学形态学的方法进行血管网络和黄斑分割。最后，在本研究中总共考虑了 13 个 HEs 特征并选择进行分类。所提出的方法在 DIARETDB0、DIARETDB1 和 MESSIDOR 数据库的 419 幅眼底图像上进行了评估。实验结果表明，病灶水平出血的总体平均敏感性、特异性、预测值和准确度分别为98.90%、99.66%、97.63%和99.56%。结果表明，所提出的方法在检测出血方面优于其他最先进的方法。