Diabetic retinopathy detection and classification using hybrid feature set
Diabetic retinopathy detection and classification using hybrid feature set
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DOI：
10.1002/jemt.23063
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发表时间：
2018-09-01
影响因子：
2.5
通讯作者：
Mufti, Muhammad Rafiq
中科院分区：
文献类型：
--
作者：
Amin, Javeria;Sharif, Muhammad;Mufti, Muhammad Rafiq
糖尿病分期复杂是糖尿病视网膜病变(DR)的主要原因，DR早期无症状，早期诊断、筛查和治疗可减少对视力的损害。本文将一种自动检测和分类技术应用于DR的检测和分类，在灰度图像上使用局部对比度增强方法来增强感兴趣区域。采用基于数学形态学的自适应阈值方法对病变区域进行精确分割。然后，将几何特征和统计特征进行融合，得到更好的分类结果。该方法在DIARETDB1、E-OPHTA、Messidor和具有不同曲线下面积(AUC)和精度(ACC)等不同度量的本地数据集上进行了验证。
Complicated stages of diabetes are the major cause of Diabetic Retinopathy (DR) and no symptoms appear at the initial stage of DR. At the early stage diagnosis of DR, screening and treatment may reduce vision harm. In this work, an automated technique is applied for detection and classification of DR. A local contrast enhancement method is used on grayscale images to enhance the region of interest. An adaptive threshold method with mathematical morphology is used for the accurate lesions region segmentation. After that, the geometrical and statistical features are fused for better classification. The proposed method is validated on DIARETDB1, E-ophtha, Messidor, and local data sets with different metrics such as area under the curve (AUC) and accuracy (ACC).