A New Method for Sperm Detection in Human Semen: Combination of Hypothesis Testing and Local Mapping of Wavelet Sub-Bands

Document Type : Original Paper

Authors

1 Electrical Engineering Department, Iranian Research Organization for Science and Technology, Tehran, Iran

2 - Electrical Engineering Department, Iran University of Science and Technology

Abstract

Introduction
Automated methods for sperm characterization in microscopic videos have some limitations such as: low contrast of the video frames and possibility of neighboring sperms to touch each other. In this paper a new method is introduced for detection of sperms in microscopic videos.
Materials and Methods
In this work, first microscopic videos are captured from specimens of human semen. Several frames of these videos are transformed to wavelet sub-bands and bit-related planes are constructed from wavelet sub-bands separately. Finally, the acquired bit planes are mapped by different local mapping functions and decision is made using continuity and discontinuity of the mapping results. Based on the above decision procedure, each region of the microscopic image is assigned to either a sperm or other parts of semen.
Results
Performance of the proposed method was evaluated by two sets of microscopic videos which have been captured from semen of some infertile men. The first sets belonged to semen specimens with low densities of sperms and the second set belonged to semen specimens with high densities of sperms.
Conclusion
The results of this study revealed that the proposed method in this work is more efficient in sperm detection and extraction compared with the current approaches in both scenarios. Furthermore, it is evident that for specimens with higher sperm densities the proposed method improved sperm detection also reduces false detection rate considerably.

Keywords

Main Subjects

References

  1. Speroff L, Fritz MA. Clinical gynecologic endocrinology and infertility: Lippincott Williams & Wilkins; 2004.
  2. Regulated Fertility Services: A commissioning aid, Department of Health, UK, June 2009. Available at: http://dh.gov.uk/prod_consum_dh/groups/dh_digitalassets/documents/digitalasset/dh_101068.pdf. Accessed Jan 28, 2013.
  3. Wenzhong Y, Shuqun S. Automatic Chromosome Counting Algorithm Based on Mathematical Morphology. Journal of Data Acquisition & Processing. 2008;23(9):1004-9037.
  4. Menkveld R, Wong WY, Lombard CJ, Wetzels AM, Thomas CM, Merkus HM, et al. Semen parameters, including WHO and strict criteria morphology, in a fertile and subfertile population: an effort towards standardization of in-vivo thresholds. Hum Reprod. 2001 Jun;16(6):1165-71.
  5. Leung C, Lu Z, Esfandiari N, Casper RF, Sun Y, editors. Detection and tracking of low contrast human sperm tail. Automation Science and Engineering (CASE), 2010 IEEE Conference on; 2010: IEEE.
  6. Shi LZ, Nascimento J, Chandsawangbhuwana C, Berns MW, Botvinick EL. Real-time automated tracking and trapping system for sperm. Microsc Res Tech. 2006 Nov;69(11):894-902.
  7. Abbiramy V, Shanthi V, Allidurai C, editors. Spermatozoa detection, counting and tracking in video streams to detect asthenozoospermia. Signal and Image Processing (ICSIP), 2010 International Conference on; 2010: IEEE.
  8. Abbiramy V, Shanthi V. Spermatozoa segmentation and morphological parameter analysis based detection of teratozoospermia. International Journal of Computer Applications IJCA. 2010;3(7):19-23.
  9. Carrillo H, Villarreal J, Sotaquira M, Goelkel M, Gutierrez R, editors. A computer aided tool for the assessment of human sperm morphology. Bioinformatics and Bioengineering, 2007 BIBE 2007 Proceedings of the 7th IEEE International Conference on; 2007: IEEE.
  10. Carrillo H, Villarreal J, Sotaquira M, Goelkel A, Gutierrez R, editors. Spermatozoon segmentation towards an objective analysis of human sperm morphology. Image and Signal Processing and Analysis, 2007 ISPA 2007 5th International Symposium on; 2007: IEEE.
  11. Acosta AA, Kruger TF.Menkveld R, Kruger T. Evaluation of sperm morphology by light microscopy. Human Spermatozoa in Assisted Reproduction. 1996:89-107.
  12. Mazzilli F, Rossi T, Delfino M, Nofroni I. Application of the upgraded image superimposition system (SIAS) to the assessment of sperm kinematics. Andrologia. 1999 Jul;31(4):187-94.
  13. Wu Q, Merchant F, Castleman KR. Microscope image processing: Academic press; 2008.
  14. Xuan ZL, Yan WZ, editors. The sperm video segmentation based on dynamic threshold. Machine Learning and Cybernetics (ICMLC), 2010 International Conference on; 2010: IEEE.
  15. Cui C. Per-Pixel Background Estimation In Video Microscopy Using Frame Grouping and Wavelet Based Image Fusion. 2009.
  16. Liu JC, Hwang WL, Chen MS, Tsai JW, Lin CH, editors. Wavelet based active contour model for object tracking. Image Processing, 2001 Proceedings 2001 International Conference on; 2001: IEEE.
  17. Yi W, Park K, Paick J, editors. Parameterized characterization of elliptic sperm heads using Fourier representation and wavelet transform. Engineering in Medicine and Biology Society, 1998 Proceedings of the 20th Annual International Conference of the IEEE; 1998: IEEE.
  18. Meijering E, Smal I, Dzyubachyk O, Olivo-Marin JC. Time-lapse imaging. Microscope Image Processing. 2008:401-40.
  19. Zimmer C, Labruyere E, Meas-Yedid V, Guillen N, Olivo-Marin JC. Segmentation and tracking of migrating cells in videomicroscopy with parametric active contours: A tool for cell-based drug testing. Medical Imaging, IEEE Transactions on. 2002;21(10):1212-21.
  20. Wählby C, SINTORN IM, Erlandsson F, Borgefors G, Bengtsson E. Combining intensity, edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections. Journal of Microscopy. 2004;215(1):67-76.
  21. Yang X, Li H, Zhou X. Nuclei segmentation using marker-controlled watershed, tracking using mean-shift, and kalman filter in time-lapse microscopy. Circuits and Systems I: Regular Papers, IEEE Transactions on. 2006;53(11):2405-14.
  22. Mallat S. A wavelet tour of signal processing. 1998. Academic, San Diego, CA.
  23. Gonzalez RC, Woods RE. Digital Image Processing: Pearson/Prentice Hall; 2008.
  24. Haralick RM, Shapiro LG. Computer and Robot Vision. Addison-Wesley Pub. Co. Boston,1992.
  25. Liu B, Chen X, Ma C, Zhang D, Zhou X, He Y, editors. Research on threshold segmentation in tracking technology of moving objects. Industrial Mechatronics and Automation (ICIMA), 2010 2nd International Conference on; 2010: IEEE.
  26. Bai W, Liu J, Chen G, editors. Segmentation of white rat sperm image. Proceedings of SPIE, the International Society for Optical Engineering; 2011: Society of Photo-Optical Instrumentation Engineers.
  27. Mortimer ST. A critical review of the physiological importance and analysis of sperm movement in mammals. Human Reproduction Update. 1997;3(5):403-39.
Iranian Journal of Medical Physics
Volume 9, Issue 4 - Serial Number 4
November and December 2012
Pages 283-292

Files

History

  • Receive Date: 29 July 2012
  • Revise Date: 16 March 2013
  • Accept Date: 25 December 2012

Share

How to cite

Statistics