ArticlePloS one2022
2D Short-Time Fourier Transform for local morphological analysis of meibomian gland images.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 13 citations in OpenAlex.
- Ambient intelligence-based framework for quantitative evaluation of cell culture operations to support standardization and training.Regenerative therapy · 2026Article
- Atypical β-strand insertion mediates the noncovalent cross-linking in amyloid aggregates.Nature communications · 2026Article
- AI driven quantitative analysis of meibomian glands in children and adolescents: a benchmark dataset study.Eye and vision (London, England) · 2025Article
- A modular and adaptable approach for automated morphological feature extraction in meibography images.Scientific reports · 2025Article
- A Deep Learning Approach for Meibomian Gland Appearance Evaluation.Ophthalmology science · 2023Article
- An effective fingerprint orientation field estimation method using differential values of grayscale intensity.PeerJ. Computer science · 2023Article
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2 authors at 1 institution in 1 country.
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Abstract
Meibography is becoming an integral part of dry eye diagnosis. Being objective and repeatable this imaging technique is used to guide treatment decisions and determine the disease status. Especially desirable is the possibility of automatic (or semi-automatic) analysis of a meibomian image for quantification of a particular gland's feature. Recent reports suggest that in addition to the measure of gland atrophy (quantified by the well-established "drop-out area" parameter), the gland's morphological changes may carry equally clinically useful information. Here we demonstrate the novel image analysis method providing detailed information on local deformation of meibomian gland pattern. The developed approach extracts from every Meibomian image a set of six morphometric color-coded maps, each visualizing spatial behavior of different morphometric parameter. A more detailed analysis of those maps was used to perform automatic classification of Meibomian glands images. The method for isolating individual morphometric components from the original meibomian image can be helpful in the diagnostic process. It may help clinicians to see in which part of the eyelid the disturbance is taking place and also to quantify it with a numerical value providing essential insight into Meibomian gland dysfunction pathophysiology.
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