9 April 2014 Identification of suitable fundus images using automated quality assessment methods
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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.
© 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)
Ugur Sevik, Ugur Sevik, Cemal Kose, Cemal Kose, Tolga Berber, Tolga Berber, Hidayet Erdol, Hidayet Erdol, } "Identification of suitable fundus images using automated quality assessment methods," Journal of Biomedical Optics 19(4), 046006 (9 April 2014). https://doi.org/10.1117/1.JBO.19.4.046006 . Submission:


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