  Meibography is a non-contact imaging technique used by ophthalmologists to
assist in the evaluation and diagnosis of meibomian gland dysfunction (MGD).
While artificial qualitative analysis of meibography images could lead to low
repeatability and efficiency and multi-parametric analysis is demanding to
offer more comprehensive information in discovering subtle changes of meibomian
glands during MGD progression, we developed an automated and multi-parametric
algorithm for objective and quantitative analysis of meibography images. The
full architecture of the algorithm can be divided into three steps: (1)
segmentation of the tarsal conjunctiva area as the region of interest (ROI);
(2) segmentation and identification of glands within the ROI; and (3)
quantitative multi-parametric analysis including newly defined gland diameter
deformation index (DI), gland tortuosity index (TI), and glands signal index
(SI). To evaluate the performance of the automated algorithm, the similarity
index (k) and the segmentation error including the false positive rate (r_P)
and the false negative rate (r_N) are calculated between the manually defined
ground truth and the automatic segmentations of both the ROI and meibomian
glands of 15 typical meibography images. The feasibility of the algorithm is
demonstrated in analyzing typical meibograhy images.
