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Validation of a Semiautomatic Optical Coherence Tomography Digital Image Processing Algorithm for Estimating the Tear
Alejandro Cardenas-Morales1, Maria Fernanda Tamez-Olvera1, Maria Paula Cervantes-Rios1
1Clinical Science Department, Science of Health Division, University of Monterrey, Monterrey, Mexico.
Translational Vision Science & Technology
|April 4, 2023
Summary
A new semiautomated algorithm accurately measures tear meniscus height (TMH) from optical coherence tomography (OCT) images. This digital image processing (DIP) technique aids in diagnosing dry eye disease with high repeatability and reproducibility.
Area of Science:
- Ophthalmology
- Medical Imaging
- Biomedical Engineering
Background:
- Dry eye disease diagnosis relies on accurate tear meniscus height (TMH) measurements.
- Current methods for TMH quantification from optical coherence tomography (OCT) images can be subjective and time-consuming.
Purpose of the Study:
- To design and validate a high-sensitivity, semiautomated algorithm for identifying and quantifying TMH from OCT images.
- To utilize digital image processing (DIP) techniques for enhanced accuracy and efficiency.
Main Methods:
- An adaptive contrast algorithm was developed, comprising two stages: region of interest selection and TMH detection/measurement.
- The algorithm employed morphologic operations and derivative image intensities.
- Algorithm performance was assessed for trueness, repeatability, and reproducibility, and compared against manual measurements using commercial software.
Main Results:
- The algorithm demonstrated excellent repeatability, with an intraclass correlation coefficient of 0.993 and a coefficient of variation of 2.96%.
- Reproducibility tests showed no significant difference between expert (244.4 ± 114.9 µm) and inexperienced observers (242.4 ± 111.2 µm).
- The algorithm's measurements strongly correlated with those obtained manually via commercial software.
Conclusions:
- The developed algorithm reliably identifies and measures TMH from OCT images with high repeatability and reproducibility.
- This DIP-based methodology offers a valuable tool for ophthalmologists in diagnosing dry eye disease, with minimal user dependency.
