Please use this identifier to cite or link to this item: https://doi.org/10.1117/1.JBO.17.8.086008
Title: Detection of meibomian glands and classification of meibography images
Authors: Koh, Y.W.
Celik, T.
Lee, H.K.
Petznick, A.
Tong, L. 
Keywords: Computer vision
Diagnosis
Dry-eye
Image processing
Machine learning
Meibography
Issue Date: Aug-2012
Citation: Koh, Y.W., Celik, T., Lee, H.K., Petznick, A., Tong, L. (2012-08). Detection of meibomian glands and classification of meibography images. Journal of Biomedical Optics 17 (8) : -. ScholarBank@NUS Repository. https://doi.org/10.1117/1.JBO.17.8.086008
Abstract: Computational methods are presented that can automatically detect the length and width of meibomian glands imaged by infrared meibography without requiring any input from the user. The images are then automatically classified. The length of the glands are detected by first normalizing the pixel intensity, extracting stationary points, and then applying morphological operations. Gland widths are detected using scale invariant feature transform and analyzed using Shannon entropy. Features based on the gland lengths and widths are then used to train a linear classifier to accurately differentiate between healthy (specificity 96.1%) and unhealthy (sensitivity 97.9%) meibography images. The user-free computational method is fast, does not suffer from inter-observer variability, and can be useful in clinical studies where large number of images needs to be analyzed efficiently. © 2012 Society of Photo-Optical Instrumentation Engineers (SPIE).
Source Title: Journal of Biomedical Optics
URI: http://scholarbank.nus.edu.sg/handle/10635/110017
ISSN: 10833668
DOI: 10.1117/1.JBO.17.8.086008
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