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

  • I Gede Susrama Masdiyasa Institut Teknologi Sepuluh November Surabaya
  • I D. G. Hari Wisana Departement of Informatics, Universitas of Pembangunan Nasional Veteran East Java
  • I K. Eddy Purnama Department of Electromedic Engineering, Politeknik Kesehatan Surabaya
  • M. Hery Purnomo Department of Computer Engineering, Institut Teknologi Sepuluh Nopember Surabaya
DOI: https://doi.org/10.24843/LKJITI.2018.v09.i01.p04

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

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References

[1] World Health Organization, WHO laboratory manual for the examination of human semen, Fifth Edition, Cambridge University Press, 2010.
[2] P. Hidayatullah and M. Zuhdi, “Automatic Sperms Counting using Adaptive Local Threshold and Ellipse Detection,” in proceeding International Conference on Informat Technology Systems and Innovation (ICITSI)-IEEE, 2014, pp. 56–61 

[3] M.Y. Khachane, R.J. Ramteke, and R.R Manza, “Fuzzy Rule Based Classification of Human Spermatozoa”, in proceeding International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO), 2015, pp. 1-5.
[4] I. G. Susrama, I. K. Eddy Purnama and M. H. Purnomo, “Teratozoospermia Classifi- cation Based on the Sperm Head Using Otsu Threshold and Decision Tree,” Journal Matec Web Of Conferences 58, 2016, pp.03012–03019. 

[5] Q. Li, X. Chen, H. Zhang, L. Yin, S. Chen, T. Wang, S. Lin, X. Liu, X. Zhang, and R. Zhang, “Automatic human spermatozoa detection in microscopic video streams based on OpenCV,” 5th International Conference on Biomedical Engineering and Informatics (BMEI), 2012, pp. 224- 227. 

[6] A. Nurhadiyatna, A. L. Latifah, D. Fryantoni, T. Wirahman, R. Wijayanti, dan F. H. Muttaqien, “Comparison and Implementation of Motion Detection Methods for Sperm Detection and Tracking”, International Symposium on Micro-Nano Mechatronics and Human Science (MHS), 2014, pp. 1-5.
[7] Y. Imani, N. Teyfouri, M. R. Ahmadzadeh and M. Golabbakhsh, “A New Method for Multiple Sperm Cells Tracking”, Journal of Medical and Signals Sensors, Vol. 4, No.1, pp. 35–42, 2014. 

[8] I. G. A. Socrates, L.A. Afrizal, A. M. Sonhaji, “Optimasi Naïve Bayes Dengan Pemilihan Fitur Dan Pembobotan Gain Ratio”, Lontar Komputer: Jurnal Ilmiah Teknologi Informasi, Vol. 7, No. 1, pp. 22-30, 2016. .
[9] N. L. W Sri Rahayu, “Deteksi Batik Parang Menggunakan Fitur Co-Occurrence Matrix Dan Geometric Moment Invariant Dengan Klasifikasi KNN”, Lontar Komputer: Jurnal Ilmiah Teknologi Informasi, Vol. 7, No. 1, pp. 22-30, 2016.
[10] A. Sobral, A. Vacavant, “A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos”, Journal Computer Vision and Image Understanding, Vol. 122, May 2014, pp. 4–21, 2014.
[11] J. Vaněk, L. Machlica, J. Psutka, “Estimation of Single-Gaussian and Gaussian Mixture Models for Pattern Recognition”, 18th Iberoamerican Congress, Proceedings CIARP, Havana, Cuba, Vol. 8258, pp. 49-56, 2013.
[12] A. Elgammal, D. Harwood, L. Davis, “Non-parametric Model for Background Subtraction”, 6th European Conference on Computer Vision, Dublin, Vol. 1843, pp. 751-767, 2000.
[13] Y. Benezeth, P-M. Jodoin, B. Emile, H. Laurent, C. Rosenberger, “Comparative study of background subtraction algorithms”, Journal of Electronic Imaging, Vol. 19, No. 3, pp. 1-31, 2010.
Published
2018-05-01
How to Cite
MASDIYASA, I Gede Susrama et al. Modified Background Subtraction Statistic Models for Improvement Detection and Counting of Active Spermatozoa Motility. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, [S.l.], p. 28-39, may 2018. ISSN 2541-5832. Available at: <https://ojs.unud.ac.id/index.php/lontar/article/view/38136>. Date accessed: 22 nov. 2024. doi: https://doi.org/10.24843/LKJITI.2018.v09.i01.p04.
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Vol. 9, No. 1 April 2018
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