The Use of Datasets of Bad Quality Images to Define Fundus Image Quality.
Journal:
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Published Date:
Jul 1, 2022
Abstract
Screening programs for sight-threatening diseases rely on the grading of a large number of digital retinal images. As automatic image grading technology evolves, there emerges a need to provide a rigorous definition of image quality with reference to the grading task. In this work, on two subsets of the CORD database of clinically grad able and matching non-grad able digital retinal images, a feature set based on statistical and on task-specific morphological features has been identified. A machine learning technique has then been demonstrated to classify the images as per their clinical gradeability, offering a proxy for a rigorous definition of image quality.
