Abstract
Purpose: To design and validate a high sensitivity semi-automated algorithm, based on adaptive contrast image, able to identify and quantify Tear Meniscus Height (TMH) from Optical Coherence Tomography (OCT) images by using digital
image processing (DIP) techniques.
Methods:OCT Images of the lacrimal meniscus of healthy patients and with dry eye, are analyzed by our algorithm which is composed by two stages: (1) The Region Of Interest (ROI) and (2) TMH detection and measurement. The algorithm
performs an adaptive contrast sequence based on morphological operations and derivative image intensities. Trueness, repeatability and reproducibility for TMH measurements are computed and the algorithm performance is statistically
compared against the corresponding obtained manually by using a commercial software.
Results:The algorithm showed excellent repeatability supported by an Intraclass Correlation Coefficient equal to 0.993, a Within Subject Standard Deviation equal to 9.88 and a Coefficient of Variation equal to 2.96%; and for the reproducibility
test, the results did not show a significant difference as the mean value of an expert observer was 244.4±114.9 µm versus 242.4±111.2 µm of the inexperienced observer (p=0.999). The method strongly suggests the algorithm can predict measurements which are manually performed with commercial software.
Conclusions: The presented algorithm possess high potential to identify and measure TMH from OCT-images in a reproducible and repeatable way with minimal dependency on user.
Translational Relevance: The presented work shows a methodology on how, by using DIP, it is possible to process OCT images to calculate TMH and aid ophthalmologists in the diagnosis of DED.
image processing (DIP) techniques.
Methods:OCT Images of the lacrimal meniscus of healthy patients and with dry eye, are analyzed by our algorithm which is composed by two stages: (1) The Region Of Interest (ROI) and (2) TMH detection and measurement. The algorithm
performs an adaptive contrast sequence based on morphological operations and derivative image intensities. Trueness, repeatability and reproducibility for TMH measurements are computed and the algorithm performance is statistically
compared against the corresponding obtained manually by using a commercial software.
Results:The algorithm showed excellent repeatability supported by an Intraclass Correlation Coefficient equal to 0.993, a Within Subject Standard Deviation equal to 9.88 and a Coefficient of Variation equal to 2.96%; and for the reproducibility
test, the results did not show a significant difference as the mean value of an expert observer was 244.4±114.9 µm versus 242.4±111.2 µm of the inexperienced observer (p=0.999). The method strongly suggests the algorithm can predict measurements which are manually performed with commercial software.
Conclusions: The presented algorithm possess high potential to identify and measure TMH from OCT-images in a reproducible and repeatable way with minimal dependency on user.
Translational Relevance: The presented work shows a methodology on how, by using DIP, it is possible to process OCT images to calculate TMH and aid ophthalmologists in the diagnosis of DED.
| Original language | English |
|---|---|
| Journal | Translational Vision Science and Technology |
| Publication status | Submitted - 2022 |