Objective: Automated, objective and fast measurement of the image quality of single retinal fundus photos to allow a stable and reliable medical evaluation.
Methods: The proposed technique maps diagnosis-relevant criteria inspired by diagnosis procedures based on the advise of an eye expert to quantitative and objective features related to image quality. Independent from segmentation methods it combines global clustering with local sharpness and texture features for classification.
Results: On a test dataset of 301 retinal fundus images we evaluated our method on a given gold standard by human observers and compared it to a state of the art approach. An area under the ROC curve of 95.3% compared to 87.2% outperformed the state of the art approach. A significant p-value of 0.019 emphasizes the statistical difference of both approaches.
Conclusions: The combination of local and global image statistics models the defined quality criteria and automatically produces reliable and objective results in determining the image quality of retinal fundus photos.