Counting the Number of Active Spermatozoa Movements Using Improvement Adaptive Background Learning Algorithm

International Journal of artificial intelligence research

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Field Value
 
Title Counting the Number of Active Spermatozoa Movements Using Improvement Adaptive Background Learning Algorithm
 
Creator Masdiyasa, I Gede Susrama
Purbasari, Intan Yuniar
Hatta, Moch.
Junaidic, Achmad
 
Subject
Counting; Spermatozoa; Movement; Adaptive Background Learning; Background Subtraction
 
Description The most important early stage in sperm infertility research is the detection of sperm objects. The success rate in separating sperm objects from semen fluid has an important role for further analysis. This research performed the detection and calculation of human spermatozoa. The detected sperm was the moving sperm in the video data. An improvement of Adaptive Background Learning was applied to detect the moving sperm. The purpose of this method is to improve the performance of Adaptive Background Learning algorithm in background subtraction process to detect and calculate moving sperm on the microscopic video of sperm fluid. This paper also compared several other background subtraction algorithms to conclude the appropriate background subtraction algorithm for sperm detection and sperm counting. The process done in this research was preprocessing using the Gaussian filter. The next was background subtraction process, followed by morphology operation. To test or validate the detection results of any background subtraction algorithm used, the foreground mask results from the morphological operation were compared to the ground truth of moving sperm image. For visualization purposes, every BLOB area (white object in binary image) on the foreground were given a bounding box to the original frame and the number of BLOB objects present in the foreground mask were counted. This shows that the system had been able to detect and calculate moving sperm. Based on the test results, Adaptive Background Learning method had a value of F-measure of 0.9205 and succeeded in extracting sperm shape close to the original form compared to other methods.
 
Publisher STMIK Dharma Wacana
 
Contributor
 
Date 2020-02-19
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://ijair.id/index.php/ijair/article/view/94
10.29099/ijair.v4i1.94
 
Source International Journal of Artificial Intelligence Research; Vol 4, No 1 (2020): June; 9-20
2579-7298
10.29099/ijair.v4i1
 
Language eng
 
Relation http://ijair.id/index.php/ijair/article/view/94/pdf
 
Rights Copyright (c) 2020 International Journal of Artificial Intelligence Research
https://creativecommons.org/licenses/by-sa/4.0
 

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