DRIMDB (Diabetic Retinopathy Images Database) Database for Quality Testing of Retinal Images

Retinal image quality assessment (IQA) is a crucial process for automated retinal image analysis systems to obtain an accurate and successful diagnosis of retinal diseases. Consequently, the first step in a good retinal image analysis system is measuring the quality of the input image. We present an approach for finding medically suitable retinal images for retinal diagnosis. We used a three-class grading system that consists of good, bad, and outlier classes. We created a retinal image quality dataset with a total of 216 consecutive images called the Diabetic Retinopathy Image Database. We identified the suitable images within the good images for automatic retinal image analysis systems using a novel method. Subsequently, we evaluated our retinal image suitability approach using the Digital Retinal Images for Vessel Extraction and Standard Diabetic Retinopathy Database Calibration level 1 public datasets. The results were measured through the F1 metric, which is a harmonic mean of pre

视网膜图像质量评估（IQA）是自动化视网膜图像分析系统获取准确且成功的视网膜疾病诊断的关键过程。因此，优秀的视网膜图像分析系统的第一步即是测量输入图像的质量。本研究提出了一种寻找适合医学诊断的视网膜图像的方法。我们采用了一个包含良好、不良和异常类别的三级评分系统。我们构建了一个包含216张连续视网膜图像的视网膜图像质量数据集，命名为糖尿病视网膜病变图像数据库。利用一种新颖的方法，我们在良好图像中识别出适合自动视网膜图像分析系统的图像。随后，我们使用数字视网膜图像用于血管提取和标准糖尿病视网膜病变数据库校准等级1的公共数据集评估了我们的视网膜图像适宜性方法。结果通过F1指标进行衡量，F1指标是精确率和召回率的调和平均数。