{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T00:05:54Z","timestamp":1790381154677,"version":"4.1.0"},"reference-count":40,"publisher":"Public Library of Science (PLoS)","issue":"6","license":[{"start":{"date-parts":[[2022,6,24]],"date-time":"2022-06-24T00:00:00Z","timestamp":1656028800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>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\u2019s feature. Recent reports suggest that in addition to the measure of gland atrophy (quantified by the well-established \u201cdrop-out area\u201d parameter), the gland\u2019s 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.<\/jats:p>","DOI":"10.1371\/journal.pone.0270473","type":"journal-article","created":{"date-parts":[[2022,6,24]],"date-time":"2022-06-24T17:40:42Z","timestamp":1656092442000},"page":"e0270473","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":8,"title":["2D Short-Time Fourier Transform for local morphological analysis of meibomian gland images"],"prefix":"10.1371","volume":"17","author":[{"given":"Kamila","family":"Ci\u0119\u017car","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0160-0895","authenticated-orcid":true,"given":"Mikolaj","family":"Pochylski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2022,6,24]]},"reference":[{"issue":"3","key":"pone.0270473.ref001","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.jtos.2017.05.008","article-title":"TFOS DEWS II definition and classification report","volume":"15","author":"J. 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