Advertisement

Human Sperm Tracking Using Improved Anti-collision Mean Shift Tracking Method

  • Weng Chun Tan
  • Nor Ashidi Mat IsaEmail author
  • Mahaneem Mohamed
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 547)

Abstract

Sperm tracking is challenging in sperm motility assessment. Most of the existing methods are not able to track the object while the collision between sperms are occurred. This paper introduces an anti-collision method to detect the collision and track sperm robustly. By using the pixel weight and moment features, the new non-occluded region is extracted to perform the tracking under collision condition. Based on the results, the proposed method is able to solve the existing drawbacks and producing low Bhattacharyya distance results compared with standard mean shift tracking method. In future, this method is expected to be implement on multiple sperm tracking and classifying sperm motility categories according to the latest WHO manual.

Keywords

Sperm motility assessment Collision Non-occluded region 

Notes

Acknowledgements

This research is supported by USM Research University (Individual) grant entitled “Development of Automated Intelligent Karyotyping System for Classifying Abnormal Chromosome” and by Ministry of Higher Education (MOHE) under MyPhD Scholarship.

References

  1. 1.
    Hu, W., Tan, T., Wang, L., Maybank, S.: A survey on visual surveillance of object motion and behaviors. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 34, 334–352 (2004).  https://doi.org/10.1109/TSMCC.2004.829274CrossRefGoogle Scholar
  2. 2.
    Abbiramy, V.S., Shanthi, V.: Spermatozoa segmentation and morphological parameter analysis based detection of teratozoospermia. Int. J. Comput. Appl. 3, 19–23 (2010)Google Scholar
  3. 3.
    Shi, L.Z., Nascimento, J.M., Berns, M.W., Botvinick, E.L.: Computer-based tracking of single sperm. J. Biomed. Opt. 11, 54009 (2006).  https://doi.org/10.1117/1.2357735CrossRefGoogle Scholar
  4. 4.
    Nafisi, V.R., Moradi, M.H., Nasr-Esfahani, M.H.: A template matching algorithm for sperm tracking and classification. Physiol. Meas. 26, 639–651 (2005).  https://doi.org/10.1088/0967-3334/26/5/006CrossRefGoogle Scholar
  5. 5.
    Kailath, T.: The divergence and Bhattacharyya distance measures in signal selection. IEEE Trans. Commun. Technol. 15, 52–60 (1967).  https://doi.org/10.1109/TCOM.1967.1089532CrossRefGoogle Scholar
  6. 6.
    Ning, J., Zhang, L., Zhang, D., Wu, C.: Scale and orientation adaptive mean shift tracking. IET Comput. Vis. 6, 52 (2012).  https://doi.org/10.1049/iet-cvi.2010.0112MathSciNetCrossRefGoogle Scholar
  7. 7.
    Khan, Z.H., Gu, I.Y.-H., Backhouse, A.G.: Robust visual object tracking using multi-mode anisotropic mean shift and particle filters. IEEE Trans. Circuits Syst. Video Technol. 21, 74–87 (2011).  https://doi.org/10.1109/tcsvt.2011.2106253

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Weng Chun Tan
    • 1
  • Nor Ashidi Mat Isa
    • 1
    Email author
  • Mahaneem Mohamed
    • 2
  1. 1.School of Electrical and Electronic EngineeringUniversiti Sains MalaysiaNibong TebalMalaysia
  2. 2.School of Medical SciencesUniversiti Sains MalaysiaKota BharuMalaysia

Personalised recommendations