TY - JOUR
T1 - Validation of a Semiautomatic Optical Coherence Tomography Digital Image Processing Algorithm for Estimating the Tear Meniscus Height
AU - Cardenas-Morales, Alejandro
AU - Tamez-Olvera, Maria Fernanda
AU - Cervantes-Rios, Maria Paula
AU - Garza-Leon, Manuel
AU - Tomasi, Matteo
AU - Tavera-Ruiz, Cesar Giovani
N1 - Funding Information:
Supported by the Research Department of the University of Monterrey.
Publisher Copyright:
© 2023 The Authors.
PY - 2023/4/1
Y1 - 2023/4/1
N2 - PURPOSE: To design and validate a high-sensitivity semiautomated 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 of two stages: (1) the region of interest and (2) TMH detection and measurement. The algorithm performs an adaptive contrast sequence based on morphologic operations and derivative image intensities. Trueness, repeatability, and reproducibility for TMH measurements are computed and the algorithm performance is statistically compared against the corresponding negative 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 was 244.4 ± 114.9 µm for an expert observer versus 242.4 ± 111.2 µm for the inexperienced observer (P = 0.999). The method strongly suggests the algorithm can predict measurements that 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 dry eye disease.
AB - PURPOSE: To design and validate a high-sensitivity semiautomated 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 of two stages: (1) the region of interest and (2) TMH detection and measurement. The algorithm performs an adaptive contrast sequence based on morphologic operations and derivative image intensities. Trueness, repeatability, and reproducibility for TMH measurements are computed and the algorithm performance is statistically compared against the corresponding negative 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 was 244.4 ± 114.9 µm for an expert observer versus 242.4 ± 111.2 µm for the inexperienced observer (P = 0.999). The method strongly suggests the algorithm can predict measurements that 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 dry eye disease.
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U2 - 10.1167/tvst.12.4.2
DO - 10.1167/tvst.12.4.2
M3 - Article
C2 - 37014649
SN - 2164-2591
VL - 12
SP - 2
JO - Translational Vision Science and Technology
JF - Translational Vision Science and Technology
IS - 4
M1 - 2
ER -