An optimal videokeratoscopic image presents a strong well-oriented pattern over the majority of the measured corneal surface. In the presence of interference, arising from reflections from eyelashes or tear film instability, the pattern's flow is disturbed and the local orientation of the area of interference is no longer coherent with the global flow. Detecting and analysing videokeratoscopic pattern interference is important when assessing tear film surface quality, break-up time and location

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... as well as designing tools that provide a more accurate static measurement of corneal topography. In this paper a set of algorithms for detecting interference patterns in videokeratoscopic images is presented. First a frequency approach is used to subtract the background information from the oriented structure and then a gradient-based analysis is used to obtain the pattern's orientation and coherence. The proposed techniques are compared to a previously reported method based on statistical block normalisation and Gabor filtering. The results indicate that the proposed technique leads, in most cases: to a better videokeratoscopic interference detection system, that for a given probability of the useful signal detection (99.7%) has a significantly lower probability of false alarm, and at the same time is computationally much more efficient than the previously reported method.