Automatic computer vision-based detection and quantitative analysis of indicative parameters for grading of diabetic retinopathy
Automatic computer vision-based detection and quantitative analysis of indicative parameters for grading of diabetic retinopathy
复制标题
基于计算机视觉的糖尿病视网膜病变分级指示参数的自动检测和定量分析
DOI：
--
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
C. Travieso
中科院分区：
文献类型：
--
作者：
Ashish Issac;M. Dutta;C. Travieso
糖尿病视网膜病变（DR）是影响眼睛的糖尿病并发症之一。如果不及早治疗，可能会导致永久性失明。目前的工作提出了一种自动检测病理学的方法，这些病理学是 DR 的指示参数，并在框架中战略性地使用它们来对疾病的严重程度进行分级。使用归一化过程突出显示明亮的病灶，然后使用各向异性扩散和强度阈值来检测病灶，这使得算法具有鲁棒性，可以正确拒绝误报。基于 SVM 的分类器使用 10 种不同的特征类型来拒绝误报。从经过阴影校正的绿色通道图像中准确检测到红色病变，然后进行形态洪水填充和区域最小值操作。使用几何特征拒绝误报可以降低系统的复杂性和计算效率。对疾病严重程度进行分级的综合定量分析得出 DIARETDB1 和 MESSIDOR 数据库的平均敏感性分别为 92.85 和 86.03%。
Diabetic retinopathy (DR) is one of the complications of diabetes affecting the eyes. If not treated at an early stage, then it can cause permanent blindness. The present work proposes a method for automatic detection of pathologies that are indicative parameters for DR and use them strategically in a framework to grade the severity of the disease. The bright lesions are highlighted using a normalization process followed by anisotropic diffusion and intensity threshold for detection of lesions which makes the algorithm robust to correctly reject false positives. SVM-based classifier is used to reject false positives using 10 distinct feature types. Red lesions are accurately detected from a shade-corrected green channel image, followed by morphological flood filling and regional minima operations. The rejection of false positives using geometrical features makes the system less complex and computationally efficient. A comprehensive quantitative analysis to grade the severity of the disease has resulted in an average sensitivity of 92.85 and 86.03% on DIARETDB1 and MESSIDOR databases, respectively.