彩色眼底图像中微动脉瘤的自动检测与分类在糖尿病视网膜病变早期诊断中的应用

M. V. Gopala Rao, V. Chandra Prakash, M. V. M. G. Guru Charan, G. Venkata Bhargav, M. N. Rao
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引用次数: 0

摘要

糖尿病视网膜病变(DR)是糖尿病患者视力障碍的主要原因之一。为了减少视力损害,需要提前诊断,从而将对眼睛的损害降到最低。在DR中,微动脉瘤(MA)是最早的医学征兆,表现为微小的个体视网膜模式。因此,需要强大的计算机辅助诊断技术来检测MA。本文提出了一种自动检测眼底图像中MAs的新方法。利用KNN、SVM和NB分类器,从候选的MAs中提取了11个基于形状和强度特征的特征,并对真假候选的MAs进行了分类。该方法在一个公开可用的数据集(E-ophtha)上进行了评估。该方法的性能通过使用灵敏度、特异性和准确性指标来衡量。实验结果表明,该方法在临床诊断中是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic Detection and Classification of Microaneurysms for Early Detection of Diabetic Retinopathy in Color Fundus Images
Diabetic Retinopathy (DR) is one of the main causes of visual disorder in patients affected with diabetes. Prior diagnosis is needed to reduce the visual impairment, so that damage to eye can be minimized. In the DR, Microaneurysm (MA) is the earliest medical sign which appears as tiny individual retinal patterns. So, powerful computer aided diagnose techniques for MA detection are needed. In this paper, a new approach for the automatic detection of MAs in eye fundus images is proposed. Eleven features based on shape and intensity characteristics are extracted from MA candidates and true MAs are classified from false candidates using KNN, SVM and NB classifiers. This proposed approach is evaluated on a publicly available dataset (E-ophtha). The performance of this method is measured by using sensitivity, specificity, and accuracy metrics. The experimental outcome demonstrated that the proposed method is efficient to diagnose clinically.
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