Ciliary beat frequency (CBF) can be a useful parameter for diagnosis of several diseases, as e.g. primary
ciliary dyskinesia. (PCD). CBF computation is usually done using manual evaluation of high speed video
sequences, a tedious, observer dependent, and not very accurate procedure. We used the OpenCV's pyramidal
implementation of the Lukas-Kanade algorithm for optical flow computation and applied this to certain objects
to follow the movements. The objects were chosen by their contrast applying the corner detection by Shi and
Tomasi. Discrimination between background/noise and cilia by a frequency histogram allowed to compute the
CBF. Frequency analysis was done using the Fourier transform in matlab. The correct number of Fourier
summands was found by the slope in an approximation curve. The method showed to be usable to distinguish
between healthy and diseased samples. However there remain difficulties in automatically identifying the cilia,
and also in finding enough high contrast cilia in the image. Furthermore the some of the higher contrast cilia
are lost (and sometimes found) by the method, an easy way to distinguish the correct sub-path of a point's path
have yet to be found in the case where the slope methods doesn't work.
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