The use of datasets of bad quality images to define fundus image quality

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Menolotto, Matteo and Giardini, Mario E.; (2022) The use of datasets of bad quality images to define fundus image quality. In: 2022 44th IEEE Engineering in Medicine and Biology Conference (EMBC). International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) . IEEE, GBR, pp. 504-507. ISBN 9781728127828 (https://doi.org/10.1109/EMBC48229.2022.9871614)

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Abstract

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 gradable and matching non-gradable 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. Clinical Relevance— This work offers a novel strategy to define fundus image quality, to contribute to the development of automatic fundus image graders for retinal screening.

ORCID iDs

Menolotto, Matteo and Giardini, Mario E. ORCID logoORCID: https://orcid.org/0000-0003-4849-9683;
CORE (COnnecting REpositories)