The use of datasets of bad quality images to define fundus image quality
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Date
2022-09-08
Authors
Menolotto, Matteo
Giardini, Mario E.
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published Version
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.
Description
Keywords
Sight-threatening diseases , Digital retinal images , Image quality
Citation
Menolotto, M. and Giardini, M. E. (2022) 'The use of datasets of bad quality images to define fundus image quality', 2022 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Glasgow, United Kingdom, 11-15 July, pp. 504-507. doi: 10.1109/EMBC48229.2022.9871614