A New Method for Multiple Sperm Cells Tracking

Abstract

Motion analysis and quality assessment of human sperm cell is of great importance for clinical applications of male infertility. Sperm tracking is quite complex due to cell collision, occlusion and missed detection. The goal of this study is simultaneous tracking of multiple human sperm cells. In the first step in this research the frame difference algorithm is used for background subtraction. There are some limitations to select an appropriate threshold value since the output accuracy is strongly dependant on the selected threshold value. To eliminate this dependency, we propose an improved nonlinear diffusion filtering in time domain. Nonlinear diffusion filtering is a smoothing and noise removing approach that can preserve edges in images.

Many sperms that move with different speeds in different directions eventually coincide. For multiple tracking over time, an optimal matching strategy is introduced that is based on the optimization of a new cost function. A Hungarian search method is utilized to obtain the best matching for all possible candidates. Results show 3.24% frame based error in dataset of videos that contain more than 1 and less than 10 sperm cells. So the accuracy rate was 96.76%. These results indicate the validity of the proposed algorithm to perform multiple sperms tracking.