Identification of suitable fundus images using automated quality assessment methods.
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.
Karadeniz Technical University, Department of Statistics and Computer Science, Faculty of Science, Trabzon 61080, Turkey.
Uğur
U
Şevik
Karadeniz Technical University, Department of Computer Engineering, Faculty of Engineering, Trabzon 61080, Turkey.
Cemal
C
Köse
Karadeniz Technical University, Department of Statistics and Computer Science, Faculty of Science, Trabzon 61080, Turkey.
Tolga
T
Berber
Karadeniz Technical University, Department of Ophthalmology, Faculty of Medicine, Trabzon 61080, Turkey.
Hidayet
H
Erdöl
D001185:Artificial Intelligence; D016208:Databases, Factual; D003930:Diabetic Retinopathy / Q000473:pathology; D005654:Fundus Oculi; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D012160:Retina / Q000473:pathology*; D012171:Retinal Vessels / Q000473:pathology*