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Published on: July 28, 2023
A probabilistic framework for content-based diagnosis of retinal disease
Kenneth W Tobin1, Mohamed Abdelrahman, Edward Chaum
1Oak Ridge National Laboratory, Oak Ridge, Tennessee, 37831-6075, USA. tobinkwjr@ornl.gov
Summary
Computer-assisted analysis of retinal images can aid in early diabetes detection. This study shows that content-based image retrieval can identify and quantify retinal pathology from large image archives.
Area of Science:
- Ophthalmology
- Medical Imaging
- Computer Science
Background:
- Diabetic retinopathy is a primary cause of blindness in working-age adults.
- Digital fundus cameras generate extensive archives of retinal images.
- Early detection of diabetes through screening is crucial.
Purpose of the Study:
- To develop a content-based image retrieval (CBIR) method.
- To test the hypothesis that retinal pathology can be identified and quantified using CBIR.
- To assess the utility of image archives for disease analysis.
Main Methods:
- Development of a CBIR system for retinal images.
- Analysis of a dataset comprising 395 fundus images.
- Inclusion of normal fundus images and 14 stratified disease states.
- Evaluation of diagnostic performance using specificity and sensitivity.
Main Results:
- The CBIR method demonstrated the ability to identify and quantify retinal pathology.
- Diagnostic results for specificity and sensitivity were obtained for various disease states.
- The study verified the potential of using large image archives for retinal pathology analysis.
Conclusions:
- Content-based image retrieval is a viable method for analyzing retinal pathology in large image archives.
- This approach supports early detection and management of diabetic retinopathy.
- The findings highlight the value of historical retinal image data for research.
