Efficient and robust segmentation and tracking of sperm cells in microscopic image sequences

Sperm motility analysis is an important factor in male fertility diagnosis. This article presents a hybrid segmentation method to detect sperm cells, which is robust to density variation of the cells in the image sequences. In addition, a preprocessing scheme is employed to remove fixed sperm cells and debris, which facilitate and speed up the cells' tracking stage. The article also proposes an automated sperm‐tracking algorithm in semen samples image sequences. It is a multi‐step tracking scheme, which is an enhanced version of adaptive window average speed (AWAS) tracking algorithm. It retrieves lost sperm cells during the tracking stage in adjacent frames and alleviates the cells collide problem. The proposed tracking algorithm provides both superior accuracy and higher speed compared to those of the other competitive algorithms for image sequences regardless of their particle densities