Automatic Detection and Tracking of Animal Sperm Cells in Microscopy Images

Sperm tracking-and-analysis is one of the interesting topics in biological research and reproductivemedicine, as it helps to assess the quality of the spermfor the male infertility. Computer-Assisted Sperm Analysis(CASA) systems provide a rapid and automated assessmentof the parameters of sperm motion, together with improvedstandardization and quality control. In this paper, we proposea method to detect and track animal sperms automatically. First, we detect the sperms in the first frame of all thesequences using a bag-of-words approach and SVM classifier. Then, the detected sperm cells are tracked in the rest ofall sequences using mean-shift. The proposed algorithm isevaluated on three videos in our datasets which have spermsas groundtruth. The experimental results show that ourmethod achieves a precision of 0.94, 0.93 and 0.96, and arecall of 0.96, 0.92, and 0.97 for the three videos respectivelyin terms of sperm detection. RMSE (Root mean square error)is calculated to evaluate our results in terms of spermstracking. The results show that we achieve high performancewith RMSE of 8.06, 9.01, and 7.09 pixels for three different videos.