Enforcing monotonic temporal evolution in dry eye images.
We address the problem of identifying dry areas in the tear film as part of a diagnostic tool for dry-eye syndrome. The requirement is to identify and measure the growth of the dry regions to provide a time-evolving map of degrees of dryness. We segment dry regions using a multi-label graph-cut algorithm on the 3D spatio-temporal volume of frames from a video sequence. To capture the fact that dryness increases over the time of the sequence, we use a time-asymmetric cost function that enforces a constraint that the dryness of each pixel monotonically increases. We demonstrate how this increases our estimation's reliability and robustness. We tested the method on a set of videos and suggest further research using a similar approach.
Yedidya
Tamir
T
The Australian National University.
Carr
Peter
P
Hartley
Richard
R
Guillon
Jean-Pierre
JP
D000465:Algorithms; D001185:Artificial Intelligence; D015352:Dry Eye Syndromes / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D009887:Ophthalmoscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014741:Video Recording / Q000379:methods*