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Primary ciliary dyskinesia implies cilia with defective or total absence of motility, which may result in sinusitis, chronic bronchitis, bronchiectasis and male infertility. Diagnosis can be difficult and is based on an abnormal ciliary beat frequency (CBF) and beat pattern. In this paper, we present a method to determine CBF of isolated cells through the analysis of phase-contrast microscopy images, estimating cilia motion by means of an optical flow algorithm. After having analyzed 28 image sequences (14 with a normal beat pattern and 14 with a dyskinetic pattern), the normal group presented a CBF of 5.2 ± 1.6 Hz, while the dyskinetic patients presented a 1.9 ± 0.9 Hz CBF. The cutoff value to classify a dyskinetic specimen was set to 3.45 Hz (sensitivity 0.86, specificity 0.93). The presented methodology has provided excellent results to objectively diagnose PCD., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Persistent respiratory syncytial virus (RSV) infections have been associated with the exacerbation of chronic inflammatory diseases, including chronic obstructive pulmonary disease (COPD). This virus infects the respiratory epithelium, leading to chronic inflammation, and induces the release of mucins and the loss of cilia activity, two factors that determine mucus clearance and the increase in sputum volume. In this study, an automatic method has been established to determine the ciliary motility activity from cell cultures by means of optical flow computation, and has been applied to 136 control cultures and to 144 RSV-infected cultures. The control group presented an average of cell surface with cilia motility per field of 41 ± 15 % (mean ± standard deviation), while the infected group presented a 11 ± 5 %, t-Student p<0.001. The cutoff value to classify a infected specimen was <17.89 % (sensitivity 0.94, specificity 0.93). This methodology has proved to be a robust technique to evaluate cilia motility in cell cultures.

Abstract: In previous works, we have studied a fast and robust object tracking system based on optical flow. To solve the occlusion problem, we have developed a combined tracking system based on optical flow and adaptive filters. The critical point of the system is the coupling between optical flow and predictive algorithms. This coupling is governed by parameters such as tolerance, the absolute value of the difference between the value of velocity calculated by the optical flow and the estimated value. In this paper, we propose the use of a fuzzy control system to solve this coupling problem between the different velocities. This technique will provide great robustness to the tracking algorithm. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we study simple algorithms for three-dimensional tracking objects in a stereo video sequence, by combining optical flow and stereo vision. This method is not able to handle the occlusion of the moving objects when they disappear due to an obstacle. To improve the performance of this method, we propose the use of adaptive filters and neural networks to predict the expected instantaneous velocities of the objects. In the previous works, this system has been successfully proved in two-dimensional tracking. [Copyright &y& Elsevier]

Abstract: One of the most important problems in 3D tracking is the occlusion effect produced by obstacles. To solve this problem, we have developed a tracking system based on optical flow and stereo vision, combined with adaptive filters to predict the expected 3D velocities of objects. The critical point of the system is the coupling between tracking and predictive algorithms. We propose the use of a fuzzy control system to solve this coupling problem between the different velocities. This technique has been previously tested in 2D video sequences, providing great robustness to the tracking algorithm. [Copyright &y& Elsevier]