A New Method for Detecting Sperms in Microscopy Images: Combination of Zernike Moments and Spatial Processing

Document Type: Original Paper


1 Department of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.

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


Introduction: In recent years, modern microscopic imaging in parallel with digital image processing techniques, have facilitated computerized semen analysis. However, in these methods, distinguishing sperms from other semen particles can be hampered by low contrast of microscopic images and the possibility of neighboring sperms touching each other.
Materials and Methods: This article introduced a new method based on combination of Zernike moments and spatial processing in sperm detection. In the first step, Zernike moments were estimated due to their rotation and noise-resistant nature to mark pixels with some chance of belonging to sperms. In the second step, pruning was executed considering the connectivity of candidates and using morphological processing, to extract sperms. The proposed algorithm was examined on microscopic images exhibiting several sperms with different morphologies.
Results: The obtained results showed the ability of the proposed method in sperm detection, such that it could detect 85% of the sperms without any false detection. In a more pragmatic situation, where false detection rate was 5%, the detection rate of the proposed algorithm increased to 94%.
Conclusion: Comparing the proposed method with watershed segmentation algorithm (WSA) and morphological contour synthesis (MCS) revealed the superiority of the proposed method to its alternatives in such a way that it detected sperms at least 3% and 13% better than WSA and MCS, respectively, without any false detection. Furthermore, the rate of false detections in the proposed algorithm was at least 4% and 14% better than its alternatives.


Main Subjects

  1. Domar A.D, Broome A, Zuttermeister PC, Seibel  M , Friedman R . The prevalence and predictability of depression in infertile women. Fertility and Sterility. 1992; (58) : 1158-63.
  2.  Maduro M.R, Lamb D.J. Understanding new genetics of male infertility. The Journal of Urology. 2002; 168(5): 2197-205.
  3. Franken D.R. How accurate is sperm morphology as an indicator of sperm function?  Andrologia. 2014; 47(6): 720-3.
  4. Oku H, Ishikawa M, Ogawa N, Shiba , Yoshida M. How to track spermatozoa using high-speed visual feedback. 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2008; 125-8.
  5. Wenzhong Y , Shuqun S. Automatic Chromosome Counting Algorithm Based on Mathematical Morphology.  Journal of Data Acquis ition and Processing. 2008; 23(9): 1004-9037.
  6. Shi .L.Z, Nascimento J.M, Chandsawangbhuwana C, Botvinick EL , Berns MW. An automatic system to study sperm motility and energetics. Biomedical Microdevices. 2008 Aug ; 10(4): 573-83.
  7. Abbiramy V, Shanthi V , Allidurai C, Spermatozoa Detection. Countingand Tracking in Video Streams to Detect Asthenozoospermia.  International Conference on Signal and Image  Processing. 2010; 265-70.
  8. Zheng L , Wang Y. The sperm video segmentation based on dynamic threshold. International Conference on Machine Learning and Cybernetics (ICMLC) . 2010.
  9. Alias M.F, Isa N.A.M, Sulaiman S.A , Zamli K.Z. Detection of Sprague Dawley Sperm Using Matching Method. Knowledge-Based.Intelligent. Information and Engineering Systems; Berlin, Germany. 2008; 5719(3): 541-7.
  10. Abbiramy V.S , Shanthi V, Spermatozoa. Segmentation and Morphological Parameter Analysis Based Detection of  Teratozoospermia. International Journal Computer Applications. 2010; 3(7): 19-23.
  11. Garcia-Olalla O,  Alegre  E, Fernandez-Robles L,  Malm P,  Bengtsson  E.  Acrosome integrity assessment of boar spermatozoa images using an early fusion of texture and contour descriptors. Computer Methods Programs in Biomedicine. 2015;120(1) :49-64.
  12. Bijar A, Mikaeili M, Khayati R , Benavent AP. Fully automatic identification and discrimination of sperm parts in microoscpic images of stained human semen smear. Journal Biomedical Science and Engineering. 2012; 5: 384-95.
  13. Chang V, Saavedra JM, Castaneda V, Sarabia L, Hitschfeld N, Hartel S. Gold-standard and improved framework for sperm head segmentation. Computer Methods Programs in Biomedicine. 2014; 117(2): 225-37.
  14. Hidayatullah P , Zuhdi M. Automatic sperms counting using adaptive local threshold and ellipse detection.
  15. International Conference on Information Technology Systems and Innovation (ICITSI). Bandung-Bail. 2014: 24-7.
  16. JiaqIan L, Kuo-Kun, Haiting D , Yifan L. Human Sperm Health Diagnosis with Principal Component Analysis and K-nearest Neighbor Algorithm. Proc International Conference on Medical Biometrics. 2014; 108-13.
  17.  Tseng TT, Li Y, Hsu C.Y, Huang H.N, Zhao M , Ding M. Computer-Assisted System with Multiple Feature Fused Support Vector Machine for Sperm Morphology Diagnosis. Biomed Research International.  2013; 2013.
  18. Chun Tan W ,  Mat Isa N.A. Segmentation and Detection of Human Spermatozoa using Modified Pulse Coupled Neural Network optimized by Particle Swarm Optimization with Mutual Information. IEEE 10th Conference on Industrial Electronics and Applications (ICIEA); 2015.
  19. Shojaedini SV, Kermani A , Nafisi V.R.  A New Method for Sperm Detection in Human Semen: Combination of Hypothesis Testing and Local Mapping of  Wavelet Sub-Bands. Iranian Journal of Medical Physics. 2012; 9(4): 283-92.
  20. Shojaedini SV. A New Method for Sperms Detection in Microscopic Images By Using Fuzzy-Based Decision. 11th Iranian Conference on Medical Physics, Tehran, Iran. 2014.
  21. Shojaedini SV , Heydari M. A New Method for Sperm Detection in Infertility Cure: Hypothesis Testing Based on Fuzzy Entropy Decision. Journal of Electrical and Computer Engineering Innovations. 2014; 2(2): 69-76.
  22. Shojaedini SV , Heydari M. Automatic Sperm Analysis in Microscopic Images of Human Semen: Segmentation Using Minimization of Information Distance. Iranian Journal of Medical Physics. 2014; 11: 284-93.
  23. Shojaedini SV , Heydari M. A New Method for Sperm Characterization for Infertility Treatment: .Hypothesis Testing by Using Combination of Watershed Segmentation and Graph Theory. Journal of medical signals and sensors. 2014; 4(4): 274-80.
  24. Shojaedini SV , Parcham K. A New Method for Detecting Sperms in Microscopy Images by Using Combination of Contours and Morphological Processing. 2nd International Conference on Knowledge-Based Research in Computer. Engineering and Information Technology ,Tehran, Iran. 2016.
  25. Shojaedini SV, Goldar AR , Soori M. Correntropy based sperm detection: a novel spatiotemporal processing for analyzing videos of human semen. Health and Technology. 2017; 1-8.
  26. Haralick RM, Shapiro LG. Computer and Robot Vision. Addison-Wesley Longman Publishing Company, Inc. Boston. 1992; 28-48.
  27. Maryellen LG, et al. Automated breast ultrasound in breast cancer screening of women with dense breasts: reader study of mammography-negative and mammography-positive cancers. American Journal of Roentgenology. 2016; 206(6): 1341-50.