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首页> 外文期刊>International Journal of Electrical and Computer Engineering >Binary operation based hard exudate detection and fuzzy based classification in diabetic retinal fundus images for real time diagnosis applications
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Binary operation based hard exudate detection and fuzzy based classification in diabetic retinal fundus images for real time diagnosis applications

机译:基于二元操作的糖尿病视网膜眼底图像的硬渗滤和模糊分类,进行实时诊断应用

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Diabetic retinopathy (DR) is one of the most considerable reasons for visual impairment. The main objective of this paper is to automatically detect and recognize DR lesions like hard exudates, as it helps in diagnosing and screening of the disease. Here, binary operation based image processing for detecting lesions and fuzzy logic based extraction of hard exudates on diabetic retinal images are discused. In the initial stage, the binary operations are used to identify the exudates. Similarly, the RGB channel space of the DR image is used to create fuzzy sets and membership functions for extracting the exudates. The membership directives obtained from the fuzzy rule set are used to detect the grade of exudates. In order to evaluate the proposed approach, experiment tests are carriedout on various set of images and the results are verified. From the experiment results, the sensitivity obtained is 98.10%, specificity is 96.96% and accuracy is 98.2%.? These results suggest that the proposed method could be a diagnostic aid for ophthalmologists in the screening for DR.
机译:糖尿病视网膜病变(DR)是视力障碍最相当大的原因之一。本文的主要目的是自动检测并识别博士病变,如硬渗透物,因为它有助于诊断和筛查该疾病。这里,用于检测病变和基于模糊逻辑的基于基于糖尿病视网膜图像上的硬渗滤物的二进制操作的图像处理。在初始阶段,二进制操作用于识别出渗出物。类似地,DR图像的RGB通道空间用于创建模糊集和隶属函数以提取渗出物。从模糊规则集中获得的会员指令用于检测渗出物的等级。为了评估所提出的方法,在各种图像上携带实验测试,并验证结果。从实验结果中,所获得的灵敏度为98.10%,特异性为96.96%,准确度为98.2%。这些结果表明,该方法可以是对博士筛查中的眼科医生的诊断辅助。

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