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Healthy and superior sperm is the main requirement for a woman to get pregnant. To find out how the quality of sperm is needed several checks. One of them is a sperm analysis test to see the movement of sperm objects, the analysis is observed using a microscope and calculated manually. The first step in analyzing the scheme is detecting and separating sperm objects. This research is detecting and calculating sperm movements in video data. To detect moving sperm, the background processing of sperm video data is essential for the success of the next process. This research aims to apply and compare some background subtraction algorithms to detect and count moving sperm in microscopic videos of sperm fluid, so we get a background subtraction algorithm that is suitable for the case of sperm detection and sperm count. The research methodology begins with the acquisition of sperm video data. Then, preprocessing using a Gaussian filter, background subtraction, morphological operations that produce foreground masks, and compared with moving sperm ground truth images for validation of the detection results of each background subtraction algorithm. It also shows that the system has been able to detect and count moving sperm. The test results show that the MoG (Mixture of Gaussian) V2 (2 Dimension Variable) algorithm has an f-measure value of 0.9449 and has succeeded in extracting sperm shape close to its original form and is superior compared to other methods. To conclude, the sperm analysis process can be done automatically and efficiently in terms of time.

Mixture gaussian V2 based microscopic movement detection of human spermatozoa