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. 2011 Jun;17(2):111-9.
doi: 10.4258/hir.2011.17.2.111. Epub 2011 Jun 30.

An Automated Measurement of Ciliary Beating Frequency using a Combined Optical Flow and Peak Detection

Woojae Kim  1 , Tae Hwa Han, Hyun Jun Kim, Man Young Park, Ku Sang Kim, Rae Woong Park
Affiliations

An Automated Measurement of Ciliary Beating Frequency using a Combined Optical Flow and Peak Detection

Woojae Kim et al. Healthc Inform Res. 2011 Jun.

Abstract

Objectives: The mucociliary transport system is a major defense mechanism of the respiratory tract. The performance of mucous transportation in the nasal cavity can be represented by a ciliary beating frequency (CBF). This study proposes a novel method to measure CBF by using optical flow.

Methods: To obtain objective estimates of CBF from video images, an automated computer-based image processing technique is developed. This study proposes a new method based on optical flow for image processing and peak detection for signal processing. We compare the measuring accuracy of the method in various combinations of image processing (optical flow versus difference image) and signal processing (fast Fourier transform [FFT] vs. peak detection [PD]). The digital high-speed video method with a manual count of CBF in slow motion video play, is the gold-standard in CBF measurement. We obtained a total of fifty recorded ciliated sinonasal epithelium images to measure CBF from the Department of Otolaryngology. The ciliated sinonasal epithelium images were recorded at 50-100 frames per second using a charge coupled device camera with an inverted microscope at a magnification of ×1,000.

Results: The mean square errors and variance for each method were 1.24, 0.84 Hz; 11.8, 2.63 Hz; 3.22, 1.46 Hz; and 3.82, 1.53 Hz for optical flow (OF) + PD, OF + FFT, difference image [DI] + PD, and DI + FFT, respectively. Of the four methods, PD using optical flow showed the best performance for measuring the CBF of nasal mucosa.

Conclusions: The proposed method was able to measure CBF more objectively and efficiently than what is currently possible.

Keywords: Cilia; Computer-Assisted Image Processing; Computer-Assisted Signal Processing; Mucociliary Clearance.

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Conflict of interest statement

No potential conflict of interest relevant to this article was reported.

Figures

Figure 1
Figure 1
Ciliary epithelial cells and ciliary movements. (A) Imaginary illustration of ciliary epithelial cells. (B) Movement of cilia.
Figure 2
Figure 2
Schematic illustration of the measurement of ciliary beat frequency (CBF). Ciliary motility is observed and recorded using a microscope and workstation. CBF of ciliary epithelium images was measured by each of four combined image processing and signal processing techniques. The measured CBFs were compared with manually measured CBF using a digital high-speed video method.
Figure 3
Figure 3
(A) Ciliary epithelial cells with cilia on the top. (B) Vector field of optical flow show the direction and magnitude of change of ciliary beating intensity from one image to the other. (C) Difference image between two consecutive frames.
Figure 4
Figure 4
Extracted signal from result by (A) optical flow and (B) difference image. Signal appears sum of vectors (normalized) using optical flow or difference of pixel using difference image.
Figure 5
Figure 5
Overall flowchart of the study and the proposed method for measurement of ciliary beat frequency (CBF). FFT: fast Fourier transform.
Figure 6
Figure 6
Box plot for comparison with difference of high speed video (HSV) and the four methods. OF: optical flow, DI: difference image, PD: peak detection, FFT: fast Fourier fransform.
Figure 7
Figure 7
Bland-Altman plots of the digital high speed video (HSV) compared with the four methods. OF: optical flow, DI: difference image, PD: peak detection, FFT: fast Fourier transform.
See this image and copyright information in PMC

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