@article{,
title= {DRIMDB (Diabetic Retinopathy Images Database) Database for Quality Testing of Retinal Images},
keywords= {fundus},
author= {},
abstract= {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 precision and recall metrics. The highest F1 scores of the IQA tests were 99.60%, 96.50%, and 85.00% for good, bad, and outlier classes, respectively. Additionally, the accuracy of our suitable image detection approach was 98.08%. Our approach can be integrated into any automatic retinal analysis system with sufficient performance scores.

Good:
https://i.imgur.com/D5unNKs.png

Bad:
https://i.imgur.com/slFzaCZ.png

Outlier:
https://i.imgur.com/eG4PDet.png},
terms= {},
license= {},
superseded= {},
url= {https://pubmed.ncbi.nlm.nih.gov/24718384/}
}