Diabetic retinopathy is one of the most common complications of diabetes. Hemorrhages, micro-aneurysms, exudates are one of the earlier signs of diabetic retinopathy. This paper proposes an algorithm of matched filter with morphological operation for the detection of lesions in the fundus retinal image. Contrast limited adaptive histogram equalization method is used for the extraction of vessels. The noise is removed from the images using matched filter. After enhancement, the thresholding is applied for vessel extraction. The threshold of the image is done by the iterative self-organizing data analysis technique algorithm method. After removal of optic disk and the blood vessels from the retinal image, the morphology method is used to easily identify different types of lesions. The diabetic retinopathy images were collected from DIARETDB1; the morphology operation method is analyzed using the metrics of sensitivity, specificity and accuracy. The proposed method's detection accuracy value for the recognition of micro-aneurysms, exudates and hemorrhages was 98.43%, 98.06% and 98.68% compared with the results of the differential evolution algorithm. Detection of lesion such as micro-aneurysms, hemorrhages and exudates was possible. When compared with the differential evolution algorithm, morphological method achieved good accuracy for the detection of diabetic retinopathy.

中文翻译：

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基于形态学操作的糖尿病视网膜病变自动检测性能分析

糖尿病视网膜病变是糖尿病最常见的并发症之一。出血、微动脉瘤、渗出液是糖尿病视网膜病变的早期迹象之一。本文提出了一种结合形态学运算的匹配滤波算法来检测眼底视网膜图像中的病变。对比度受限的自适应直方图均衡方法用于血管的提取。使用匹配滤波器从图像中去除噪声。增强后，阈值用于血管提取。图像的阈值是通过迭代自组织数据分析技术算法方法完成的。从视网膜图像中去除视盘和血管后，形态学方法用于轻松识别不同类型的病变。糖尿病视网膜病变图像来自DIARETDB1；使用灵敏度、特异性和准确度的度量来分析形态学操作方法。与差分进化算法的结果相比，该方法对微动脉瘤、渗出液和出血的识别准确率分别为98.43%、98.06%和98.68%。可以检测微动脉瘤、出血和渗出液等病变。与差分进化算法相比，形态学方法对糖尿病视网膜病变的检测取得了较好的准确率。可以检测微动脉瘤、出血和渗出液等病变。与差分进化算法相比，形态学方法对糖尿病视网膜病变的检测取得了较好的准确率。可以检测微动脉瘤、出血和渗出液等病变。与差分进化算法相比，形态学方法对糖尿病视网膜病变的检测取得了较好的准确率。