Mixture gaussian V2 based microscopic movement detection of human spermatozoa

(1) Ariyono Setiawan Mail (Department of Air Transportation Management, Aviation Polytechnic of Surabaya, Indonesia)
(2) * I Gede Susrama Mas Diyasa Mail (Department of Informatics Engineering, Universitas Pembangunan Nasional “Veteran” Jatim, Indonesia)
(3) Moch Hatta Mail (Department of Computer Engineering, Universitas Maarif Hasyim Latif Sidoarjo, Indonesia)
(4) Eva Yulia Puspaningrum Mail (Department of Informatics Engineering, Universitas Pembangunan Nasional “Veteran” Jatim, Indonesia)
*corresponding author


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.


Microscopic video; Mixture of Gaussian V2;Movement detection; Spermatozoa




Article metrics

Abstract views : 757 | PDF views : 191




Full Text



[1] W. H. Organisation, WHO laboratory manual for the examination of human semen and sperm-cervical mucus interaction, fifth edition, Cambridge university press, 2010, available at: Google Scholar.

[2] I. G. S. Masdiyasa, I. K. E. Purnama, and M. H. Purnomo, “Teratozoospermia Classification Based on the Shape of Sperm Head Using OTSU Threshold and Decision Tree,” MATEC Web Conf., vol. 58, p. 03012, May 2016, doi: 10.1051/matecconf/20165803012.

[3] I. G. Susrama, K. E. Purnama, and M. H. Purnomo, “Automated Analysis of Human Sperm Number and Concentration (Oligospermia) Using Otsu Threshold Method and Labelling,” IOP Conf. Ser. Mater. Sci. Eng., vol. 105, p. 012038, Jan. 2016, doi: 10.1088/1757-899X/105/1/012038.

[4] M. U. Daloglu and A. Ozcan, “Computational imaging of sperm locomotion,” Biol. Reprod., vol. 97, no. 2, pp. 182–188, Aug. 2017, doi: 10.1093/biolre/iox086.

[5] B. J. Walker, K. Ishimoto, and R. J. Wheeler, “Automated identification of flagella from videomicroscopy via the medial axis transform,” Sci. Rep., vol. 9, no. 1, p. 5015, Dec. 2019, doi: 10.1038/s41598-019-41459-9.

[6] P. Hidayatullah, T. L. E. R. Mengko, and R. Munir, “A Survey on Multisperm Tracking for Sperm Motility Measurement,” Int. J. Mach. Learn. Comput., vol. 7, no. 5, pp. 144–151, Oct. 2017, doi: 10.18178/ijmlc.2017.7.5.637.

[7] I. G. Susrama, Tobing L, I. D. G. H. Wisana, I. K. E. Purnama, and M. H. Purnomo, “Counting the Amount of Spermatozoa Active Perframe Video Using Morphology and Local Adaptive Threshold,” in Nusantara Science and Technology Proceedings, 2018, pp. 118–126, doi: 10.11594/nstp.2018.0118.

[8] F. Ghasemian, S. A. Mirroshandel, S. Monji-Azad, M. Azarnia, and Z. Zahiri, “An efficient method for automatic morphological abnormality detection from human sperm images,” Comput. Methods Programs Biomed., vol. 122, no. 3, pp. 409–420, Dec. 2015, doi: 10.1016/j.cmpb.2015.08.013.

[9] R. R. Maggavi, S. A. Pujari, and V. C.N, “Motility Analysis with Morphology: Study Related to Human Sperm,” Procedia Comput. Sci., vol. 152, pp. 179–185, 2019, doi: 10.1016/j.procs.2019.05.041.

[10] M. F. Keskenler, A. Hasiloglu, G. T. Ozyer, B. Ozyer, and E. Simsek, “Sperm Detection and Analysis Using Feature Description Algorithms,” in 2019 27th Signal Processing and Communications Applications Conference (SIU), 2019, pp. 1–4, doi: 10.1109/SIU.2019.8806287.

[11] Q. Li et al., “Automatic human spermatozoa detection in microscopic video streams based on OpenCV,” in 2012 5th International Conference on BioMedical Engineering and Informatics, 2012, pp. 224–227, doi: 10.1109/BMEI.2012.6513003.

[12] M. Y. Khachane, R. R. Manza, and R. J. Ramteke, “Fuzzy rule based classification of human spermatozoa,” in 2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO), 2015, pp. 1–5, doi: 10.1109/EESCO.2015.7253656.

[13] Kshema, M. J. George, and D. A. S. Dhas, “Preprocessing filters for mammogram images: A review,” in 2017 Conference on Emerging Devices and Smart Systems (ICEDSS), 2017, pp. 1–7, doi: 10.1109/ICEDSS.2017.8073694.

[14] S. Kumar and J. Sen Yadav, “Background Subtraction Method for Object Detection and Tracking,” 2017, pp. 1057–1063, doi: 10.1007/978-981-10-1708-7_125.

[15] A. . Raid, W. Khedr, M. . El-dosuky, and M. Aoud, “Image Restoration Based on Morphological Operations,” Int. J. Comput. Sci. Eng. Inf. Technol., vol. 4, no. 3, pp. 9–21, Jun. 2014, doi: 10.5121/ijcseit.2014.4302.

[16] W. Piao, Y. Yuan, and H. Lin, “A Digital Image Denoising Algorithm Based on Gaussian Filtering and Bilateral Filtering,” ITM Web Conf., vol. 17, p. 01006, Feb. 2018, doi: 10.1051/itmconf/20181701006.

[17] L. Cadena, A. Zotin, and F. Cadena, “Enhancement of medical image using spatial optimized filters and OpenMP technology,” in Lecture Notes in Engineering and Computer Science, 2018, available at: Google Scholar.

[18] S.-Y. Chiu, C.-C. Chiu, and S. Xu, “A Background Subtraction Algorithm in Complex Environments Based on Category Entropy Analysis,” Appl. Sci., vol. 8, no. 6, p. 885, May 2018, doi: 10.3390/app8060885.

[19] I. G. S. Masdiyasa, I. D. G. Hari Wisana, I. K. Eddy Purnama, and M. Hery Purnomo, “Modified Background Subtraction Statistic Models for Improvement Detection and Counting of Active Spermatozoa Motility,” Lontar Komput. J. Ilm. Teknol. Inf., p. 28, May 2018, doi: 10.24843/LKJITI.2018.v09.i01.p04.

[20] A. A. Moulay and A. Amine, “A Novel Background Subtraction Algorithm for Person Tracking Based on K-NN,” in Computer Science & Information Technology (CS & IT), 2017, pp. 125–136, doi: 10.5121/csit.2017.70113.

[21] I. G. S. Masdiyasa, I. K. E. Purnama, and M. H. Purnomo, “A new method to improve movement tracking of human sperms,” IAENG Int. J. Comput. Sci., vol. 45, no. 4, pp. 1–9, 2018, available at: Google Scholar.

[22] H. B. Basoeki, A. D. Wibawa, and I. K. E. Purnama, “Improving sperms detection and counting using single Gaussian background subtraction,” in 2016 International Seminar on Application for Technology of Information and Communication (ISemantic), 2016, pp. 295–299, doi: 10.1109/ISEMANTIC.2016.7873854.

[23] R. Azzam, M. S. Kemouche, N. Aouf, and M. Richardson, “Efficient visual object detection with spatially global Gaussian mixture models and uncertainties,” J. Vis. Commun. Image Represent., vol. 36, pp. 90–106, Apr. 2016, doi: 10.1016/j.jvcir.2015.11.009.

[24] A. N. Rumaksari, S. Sumpeno, and A. D. Wibawa, “Background subtraction using spatial mixture of Gaussian model with dynamic shadow filtering,” in 2017 International Seminar on Intelligent Technology and Its Applications (ISITIA), 2017, pp. 296–301, doi: 10.1109/ISITIA.2017.8124098.

[25] N. Kim, M. Heo, R. Fleysher, C. A. Branch, and M. L. Lipton, “A Gaussian Mixture Model Approach for Estimating and Comparing the Shapes of Distributions of Neuroimaging Data: Diffusion-Measured Aging Effects in Brain White Matter,” Front. Public Heal., vol. 2, Apr. 2014, doi: 10.3389/fpubh.2014.00032.

[26] S. Qi, T. Nie, Q. Li, Z. He, D. Xu, and Q. Chen, “A Sperm Cell Tracking Recognition and Classification Method,” in 2019 International Conference on Systems, Signals and Image Processing (IWSSIP), 2019, pp. 163–167, doi: 10.1109/IWSSIP.2019.8787312.

[27] G. Gao, H. Jiang Inc., J. C. Vink, C. Chen, Y. El Khamra Inc., and J. Ita Inc., “Gaussian Mixture Model Fitting Method For Uncertainty Quantification By Conditioning To Production Data,” 2018, doi: 10.3997/2214-4609.201802279.

[28] Y. He, J. Jiang, D. Dai, and K. Fabrice, “An Incremental Kernel Density Estimator for Data Stream Computation,” Complexity, vol. 2020, pp. 1–17, Feb. 2020, doi: 10.1155/2020/1803525.

[29] T. Trnovszký, P. Sýkora, and R. Hudec, “Comparison of Background Subtraction Methods on Near Infra-Red Spectrum Video Sequences,” Procedia Eng., vol. 192, pp. 887–892, 2017, doi: 10.1016/j.proeng.2017.06.153.

[30] M. A. Soeleman et al., “An Improvement for Background Modelling using a Mixture of Gaussian and Region Growing in Moving Objects Detection,” J. Phys. Conf. Ser., vol. 1430, p. 012032, Jan. 2020, doi: 10.1088/1742-6596/1430/1/012032.

[31] A. Ajmal and I. M. Hussain, “Vehicle detection using morphological image processing technique,” in 2010 International Conference on Multimedia Computing and Information Technology (MCIT), 2010, pp. 65–68, doi: 10.1109/MCIT.2010.5444851.

[32] I. G. S. M. Diyasa, E. Y. Puspaningrum, M. Hatta, and A. Setiawan, “New Method For Classification Of Spermatozoa Morphology Abnormalities Based On Macroscopic Video Of Human Semen,” in 2019 International Seminar on Application for Technology of Information and Communication (iSemantic), 2019, pp. 133–140, doi: 10.1109/ISEMANTIC.2019.8884348.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

International Journal of Advances in Intelligent Informatics
ISSN 2442-6571  (print) | 2548-3161 (online)
Organized by Informatics Department - Universitas Ahmad Dahlan, and ASCEE Computer Society
Published by Universitas Ahmad Dahlan
W: http://ijain.org
E: info@ijain.org (paper handling issues)
   andri.pranolo.id@ieee.org (publication issues)

View IJAIN Stats

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0