Quantitative analysis of sperm motion kinematics from real-time video-edge images

A new model of sperm swimming kinematics, which uses signal processing methods and multivariate statistical techniques to identify individual cell-motion parameters and unique cell populations, is presented. Swimming paths of individual cells are obtained using real-time, video-edge digitization. Raw paths are adaptively filtered to identify average paths, and measurements of space-time oscillations about average paths are made. Time-dependent frequency information is extracted from spatial variations about average paths using harmonic analysis. Raw-path and average-path measures such as curvature, curve length, and straight-line length, and measures of oscillations about average paths such as time-dependent amplitude and frequency variations, are used in a multivariate, cluster analysis to identify unique cell populations. The entire process, including digitization of sperm video images, is computer-automated. Preliminary results indicate that this method of tracking, digitization, and kinematic analysis accurately identifies unique cell subpopulations, including: the relative numbers of cells in each subpopulation, how subpopulations differ, and the extent and significance of such differences. With appropriate work, this approach may be useful for clinical discrimination between normal and abnormal semen specimens. © 1988 SPIE.

Duke Scholars

Author David F. Katz Biomedical Engineering