Mixture gaussian V2 based microscopic movement detection of human spermatozoa

Abstract

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.

Sentiment Analysis for the Brazilian Anesthesiologist Using Multi-Layer Perceptron Classifier and Random Forest Methods

“…Random Forest, is a method developed from the CART (Classification and Regression Trees) method, which is also a method or algorithm of the decision tree technique [27]. What distinguishes the random forest method from the CART method is that Random Forest applies the bootstrap aggregating (bagging) method and also random feature selection or can be called random feature selection [28].…”

Abnormality Determination of Spermatozoa Motility Using Gaussian Mixture Model and Matching-based Algorithm

“…This fertility level determines the ability to obtain offspring [7]. One of the ways that can be used to analyze sperm is by looking at the movement of spermatozoa in semen [8][9] [10]. This way is commonly done by manually testing with visual microscopy using a microscope [11] [12].…”