A procedure to locate the eyelid position in noisy videokeratoscopic images

In this paper, we propose a new procedure to robustly determine the eyelid position in high-speed videokeratoscopic images. This knowledge is crucial in videokeratoscopy to study the effects of the eyelids on the cornea and on the tear film dynamics. Difficulties arise due to the very low contrast of videokeratoscopic images and because of the occlusions caused by the eyelashes. The proposed procedure uses robustM-estimation to fit a parametric model to a set of eyelid edge candidate pixels. To detect these pixels, firstly, nonlinear image filtering operations are performed to remove the eyelashes. Secondly, we propose an image segmentation approach based on morphological operations and active contours to provide the set of candidate pixels. Subsequently, a verification procedure reduces this set to pixels that are likely to contribute to an accurate fit of the eyelid edge. We propose a complete framework, for which each stage is evaluated using real-world videokeratoscopic images. This methodology allows for automatic localization of the eyelid edges and is applicable to replace the currently used time-consuming manual labeling approach, while maintaining its accuracy.