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IJMLC 2017 Vol.7(5): 144-151 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2017.7.5.637

A Survey on Multisperm Tracking for Sperm Motility Measurement

Priyanto Hidayatullah, Tati L. E. R. Mengko, and Rinaldi Munir

Abstract—Sperm motility is the main criterion in evaluating the quality of semen. Sperm motility measurements can be done in many ways. But the most effective way is to simultaneously track all sperm and calculate the motility parameters of Computer Aided Sperm Analysis (CASA). Based on those parameters, the sperm motility was categorized and the percentage of motile sperm was calculated. This paper presents the analysis of the currently available multisperm tracking methods for sperm motility measurement. In this paper, we discuss why sperm motility is an important parameter for assessing sperm quality and compare several multisperm tracking methods along with an analysis of their advantages and disadvantages. It can be concluded that the main problem in sperm motility measurement is having a good multisperm tracking to obtain precise sperm paths with an efficient computation on semen with high sperm concentrations. If the generated path precision is high, then the CASA parameters calculation results will better describe the actual sperm motility conditions. None of the existing methods can produce precise trajectories in complex cases yet. The complex case is, especially, when sperms collide or cover each other in the situation of large sperm counts appears in one microscope field of view.

Index Terms—Multisperm tracking, sperm motility, object tracking, CASA.

Priyanto Hidayatullah, Tati L. E. R. Mengko, and Rinaldi Munir are with School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Jl. Ganesha no. 10 Bandung, Jawa Barat, Indonesia, 40132 (e-mail: priyanto@polban.ac.id, tati@stei.itb.ac.id, rinaldi@ informatika.org).


Cite: Priyanto Hidayatullah, Tati L. E. R. Mengko, and Rinaldi Munir, "A Survey on Multisperm Tracking for Sperm Motility Measurement," International Journal of Machine Learning and Computing vol. 7, no. 5, pp. 144-151, 2017.

General Information

  • ISSN: 
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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