Advancing Meibography Assessment and Automated Meibomian Gland Detection Using Gray Value Profiles

<b>Objective</b>: This study introduces a novel method for the automated detection and quantification of meibomian gland morphology using gray value distribution profiles. The approach addresses limitations in traditional manual and deep learning-based meibography analysis, which are oft...

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Main Authors: Riccardo Forni, Ida Maruotto, Anna Zanuccoli, Riccardo Nicoletti, Luca Trimigno, Matteo Corbellino, Sònia Travé-Huarte, Giuseppe Giannaccare, Paolo Gargiulo
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Diagnostics
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Online Access:https://www.mdpi.com/2075-4418/15/10/1199
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author Riccardo Forni
Ida Maruotto
Anna Zanuccoli
Riccardo Nicoletti
Luca Trimigno
Matteo Corbellino
Sònia Travé-Huarte
Giuseppe Giannaccare
Paolo Gargiulo
author_facet Riccardo Forni
Ida Maruotto
Anna Zanuccoli
Riccardo Nicoletti
Luca Trimigno
Matteo Corbellino
Sònia Travé-Huarte
Giuseppe Giannaccare
Paolo Gargiulo
author_sort Riccardo Forni
collection DOAJ
description <b>Objective</b>: This study introduces a novel method for the automated detection and quantification of meibomian gland morphology using gray value distribution profiles. The approach addresses limitations in traditional manual and deep learning-based meibography analysis, which are often time-consuming and prone to variability. <b>Methods</b>: This study enrolled 100 volunteers (mean age 40 ± 16 years, range 18–85) who suffered from dry eye and responded to the Ocular Surface Disease Index questionnaire for scoring ocular discomfort symptoms and infrared meibography for capturing imaging of meibomian glands. By leveraging pixel brightness variations, the algorithm provides real-time detection and classification of long, medium, and short meibomian glands, offering a quantitative assessment of gland atrophy. <b>Results</b>: A novel parameter, namely “atrophy index”, a quantitative measure of gland degeneration, is introduced. Atrophy index is the first instrumental measurement to assess single- and multiple-gland morphology. <b>Conclusions</b>: This tool provides a robust, scalable metric for integrating quantitative meibography into clinical practice, making it suitable for real-time screening and advancing the management of dry eyes owing to meibomian gland dysfunction.
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spelling doaj-art-1ff2a84007764c6ba45035fae6e9a8242025-08-20T01:56:19ZengMDPI AGDiagnostics2075-44182025-05-011510119910.3390/diagnostics15101199Advancing Meibography Assessment and Automated Meibomian Gland Detection Using Gray Value ProfilesRiccardo Forni0Ida Maruotto1Anna Zanuccoli2Riccardo Nicoletti3Luca Trimigno4Matteo Corbellino5Sònia Travé-Huarte6Giuseppe Giannaccare7Paolo Gargiulo8Institute of Biomedical and Neural Engineering, Reykjavik University, 102 Reykjavik, IcelandInstitute of Biomedical and Neural Engineering, Reykjavik University, 102 Reykjavik, IcelandEspansione Group, 40050 Bologna, ItalyEspansione Group, 40050 Bologna, ItalyEspansione Group, 40050 Bologna, ItalyEspansione Group, 40050 Bologna, ItalyOptometry and Vision Sciences Research Group, Aston University, Birmingham B4 7ET, UKEye Clinic, Department of Surgical Sciences, University of Cagliari, 09123 Cagliari, ItalyInstitute of Biomedical and Neural Engineering, Reykjavik University, 102 Reykjavik, Iceland<b>Objective</b>: This study introduces a novel method for the automated detection and quantification of meibomian gland morphology using gray value distribution profiles. The approach addresses limitations in traditional manual and deep learning-based meibography analysis, which are often time-consuming and prone to variability. <b>Methods</b>: This study enrolled 100 volunteers (mean age 40 ± 16 years, range 18–85) who suffered from dry eye and responded to the Ocular Surface Disease Index questionnaire for scoring ocular discomfort symptoms and infrared meibography for capturing imaging of meibomian glands. By leveraging pixel brightness variations, the algorithm provides real-time detection and classification of long, medium, and short meibomian glands, offering a quantitative assessment of gland atrophy. <b>Results</b>: A novel parameter, namely “atrophy index”, a quantitative measure of gland degeneration, is introduced. Atrophy index is the first instrumental measurement to assess single- and multiple-gland morphology. <b>Conclusions</b>: This tool provides a robust, scalable metric for integrating quantitative meibography into clinical practice, making it suitable for real-time screening and advancing the management of dry eyes owing to meibomian gland dysfunction.https://www.mdpi.com/2075-4418/15/10/1199gray value analysismeibographymeibomian glandsdry eyemeibomian gland dysfunction
spellingShingle Riccardo Forni
Ida Maruotto
Anna Zanuccoli
Riccardo Nicoletti
Luca Trimigno
Matteo Corbellino
Sònia Travé-Huarte
Giuseppe Giannaccare
Paolo Gargiulo
Advancing Meibography Assessment and Automated Meibomian Gland Detection Using Gray Value Profiles
Diagnostics
gray value analysis
meibography
meibomian glands
dry eye
meibomian gland dysfunction
title Advancing Meibography Assessment and Automated Meibomian Gland Detection Using Gray Value Profiles
title_full Advancing Meibography Assessment and Automated Meibomian Gland Detection Using Gray Value Profiles
title_fullStr Advancing Meibography Assessment and Automated Meibomian Gland Detection Using Gray Value Profiles
title_full_unstemmed Advancing Meibography Assessment and Automated Meibomian Gland Detection Using Gray Value Profiles
title_short Advancing Meibography Assessment and Automated Meibomian Gland Detection Using Gray Value Profiles
title_sort advancing meibography assessment and automated meibomian gland detection using gray value profiles
topic gray value analysis
meibography
meibomian glands
dry eye
meibomian gland dysfunction
url https://www.mdpi.com/2075-4418/15/10/1199
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