Asset Details
Do you wish to reserve the book?
A modular and adaptable approach for automated morphological feature extraction in meibography images
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
A modular and adaptable approach for automated morphological feature extraction in meibography images
Do you wish to request the book?
A modular and adaptable approach for automated morphological feature extraction in meibography images
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
A modular and adaptable approach for automated morphological feature extraction in meibography images
2025
Overview
This study presents a modular and adaptable approach for the automated extraction of morphological features from meibography images, focusing on Meibomian gland (MG) analysis. The proposed method leverages piecewise linear modeling to derive clinically interpretable metrics that capture key structural characteristics of MGs. The workflow consists of three main stages: (1) semi-automated region of interest (ROI) selection, (2) MG identification and segmentation, and (3) extraction of gland- and image-level metrics. The approach was validated using 616 meibography images from two different imaging systems, demonstrating robustness, adaptability, and high classification accuracy for Meiboscale grading. Key metrics such as the shortening ratio and dropout area proved effective in distinguishing different stages of Meibomian gland dysfunction (MGD). By balancing automation, interpretability, and computational efficiency, this method provides a practical and scalable tool for the objective assessment of MG morphology, with potential applications in clinical practice and large-scale ophthalmic research.
Publisher
Nature Publishing Group UK,Nature Portfolio
Subject
