Paper
24 February 2012 Automatic analysis of ciliary beat frequency using optical flow
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Abstract
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.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Michael Figl, Manuel Lechner, Tobias Werther, Fritz Horak, Johann Hummel, and Wolfgang Birkfellner "Automatic analysis of ciliary beat frequency using optical flow", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 831453 (24 February 2012); https://doi.org/10.1117/12.911616
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Cited by 1 scholarly publication.
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KEYWORDS
Video

Fourier transforms

Optical flow

Motion detection

Corner detection

MATLAB

Radiation oncology

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