Feature extraction and classification of retinal images for automated detection of Diabetic Retinopathy

R. Harini, N. Sheela
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引用次数: 29

Abstract

The disorders related to retina of the eye like Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD), and Glaucoma etc., can cause visual impairments. These disorders can be diagnosed by the ophthalmologists with the help of the Digital image processing. The retinal fundus images of the patients are procured by capturing the fundus of the eye with a digital fundus camera. The Automated method of disease detection can be used against the manual method of observing several retinal fundus images to save time. In this paper a method for DR detection by utilizing Fuzzy C-Means (FCM) clustering and morphological image processing is proposed. The image pre-processing includes image resizing, CLAHE, contrast adjustment, gray and green channel extraction from the color fundus image. The classification by Support Vector Machine (SVM) classifier using selected features achieves an Accuracy of 96.67%, Sensitivity of 100%, and Specificity of 95.83%.
用于糖尿病视网膜病变自动检测的视网膜图像特征提取与分类
糖尿病视网膜病变(DR)、老年性黄斑变性(AMD)、青光眼等与视网膜有关的疾病会导致视力受损。这些疾病可以由眼科医生在数字图像处理的帮助下诊断。用数码眼底相机捕捉眼底,获取患者视网膜眼底图像。自动化的疾病检测方法可以用来对抗人工方法观察几个视网膜眼底图像,以节省时间。本文提出了一种基于模糊c均值(FCM)聚类和形态学图像处理的DR检测方法。图像预处理包括图像大小调整、CLAHE、对比度调整、提取彩色眼底图像的灰度和绿色通道。支持向量机(SVM)分类器利用所选特征进行分类,准确率为96.67%,灵敏度为100%,特异性为95.83%。
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