Automatic dry eye detection.
Dry Eye Syndrome is a common disease in the western world, with effects from uncomfortable itchiness to permanent damage to the ocular surface. Nevertheless, there is still no objective test that provides reliable results. We have developed a new method for the automated detection of dry areas in videos taken after instilling fluorescein in the tear film. The method consists of a multi-step algorithm to first locate the iris in each image, then align the images and finally analyze the aligned sequence in order to find the regions of interest. Since the fluorescein spreads on the ocular surface of the eye the edges of the iris are fuzzy making the detection of the iris challenging. We use RANSAC to first detect the upper and lower eyelids and then the iris. Then we align the images by finding differences in intensities at different scales and using a least squares optimization method (Levenberg-Marquardt), to overcome the movement of the iris and the camera. The method has been tested on videos taken from different patients. It is demonstrated to find the dry areas accurately and to provide a measure of the extent of the disease.
The Australian National University, and National ICT Australia. tamir@rsise.anu.edu.au
Tamir
T
Yedidya
Richard
R
Hartley
Jean-Pierre
JP
Guillon
Yogesan
Y
Kanagasingam
D000465:Algorithms; D001185:Artificial Intelligence; D015352:Dry Eye Syndromes; D005451:Fluorescein Angiography / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity