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Modified Background Subtraction Statistic Models for Improvement Detection and Counting of Active Spermatozoa Motility

Authors :
I Gede Susrama Masdiyasa
I D. G. Hari Wisana
I K. Eddy Purnama
M. Hery Purnomo
Source :
Lontar Komputer, Pp 28-39 (2018)
Publication Year :
2018
Publisher :
Udayana University, Institute for Research and Community Services, 2018.

Abstract

An important early stage in the research of sperm analysis is the phase of sperm detection or separating sperm objects from images/video obtained from observations on semen. The success rate in separating sperm objects from semen fluids has an important role for further analysis of sperm objects. Algorithm or Background subtraction method is a process that can be used to separate moving objects (foreground) and background on sperm video data that tend to uni-modal. In this research, some of the subproject model statistics of substrata model are Gaussian single, Gaussian Mixture Model (GMM), Kernel Density Estimation and compared with some basic subtraction model background algorithm in detecting and counting the number of active spermatozoa. From the results of the tests, the Grimson GMM method has an f-measure value of 0.8265 and succeeded in extracting the sperm form near its original form compared to other methods

Details

Language :
English
ISSN :
20881541 and 25415832
Database :
Directory of Open Access Journals
Journal :
Lontar Komputer
Publication Type :
Academic Journal
Accession number :
edsdoj.494e0dcdc2504be8a442df7ef251c3bb
Document Type :
article
Full Text :
https://doi.org/10.24843/LKJITI.2018.v09.i01.p04