Work

Clustering-Based Analysis of Meibomian Gland Morphology for Automated Assessment of Meibomian Gland Dysfunction Using the MGD-1k Dataset

Year: 2025

Type: article

Abstract: Purpose: To develop a clustering-based framework for automated categorization of Meibomian gland morphology, addressing subjectivity in Meibomian Gland Dysfunction (MGD) diagnosis. Methods: The MGD-1k dataset of 1,000 infrared meibography images was analyzed using k-means clustering to classify gland areas into three groups: low (Cluster 1), medium (Cluster 2), and high (Cluster 3). Pixel-based segmentation determined white pixel counts as a surrogate for gland area. Statistical validation, including ANOVA, was performed, and results were visualized with scatter plots, bar charts, and box plots. Results: Clustering identified 417, 358, and 225 images in Clusters 1, 2, and 3, comprising 41.7%, 35.8%, and 22.5% of the dataset, respectively. Mean pixel counts were 39,123.99 (SD = 4,213.89), 23,960.30 (SD = 5,930.49), and 54,413.95 (SD = 6,378.15). ANOVA confirmed significant inter-cluster differences (p < 0.0001). Conclusions: This framework objectively quantifies gland morphology, enabling severity stratification. The approach offers scalable diagnostic support and could be integrated with clinical tools or AI models. Future validation on diverse datasets is needed to confirm its broader applicability.

Source: Journal of Neonatal Surgery

DOI: 10.52783/jns.v14.1777

Authors: Anantha Krishnan, Salman Sarkar, Laxman Badavath

Institutions: Bangor University, Shree Guru Gobind Singh Tricentenary University, All India Institute of Medical Sciences, Mangalagiri

Language: English

Topic: Ocular Surface and Contact Lens

Subfield: Public Health, Environmental and Occupational Health

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: diamond

Funders: Gwangju Institute of Science and Technology, Catholic University of Korea