Bayesian Pixel Density Estimation Modeling to Detect Human Sperm Sample Image Based on Sperm Head Shape

Authors

  • Candra Zonyfar Universitas Buana Perjuangan Karawang
  • Kiki Ahmad Baihaqi Universitas Buana Perjuangan Karawang

DOI:

10.33395/sinkron.v6i1.11148

Keywords:

Human Sperm Heads (HSH), Classification, Hu moment, Zernike moment, Fourier descriptor

Abstract

Currently, there is a problem of the difficulty in classifying human sperm head sample images using different databases and measuring the accuracy of several different datasets. This study proposes a Bayesian Density Estimation-based model for detecting human sperm heads with four classification labels, namely, normal, tapered, pyriform, and small or amorphous. This model was applied to three kinds of datasets to detect the level of pixel density in images containing normal human sperm head samples. Experimental results and computational accuracy are also presented. As a method, this study labeled each human sperm head based on three shape descriptors using the formulas of Hu moment, Zernike moment, and Fourier descriptor. Each descriptor was also tested in the experiment. There was an increased accuracy that reached 90% after the model was applied to the three datasets. The Bayesian Density Estimation model could classify images containing human sperm head samples. The correct classification level was obtained when the human sperm head was detected by combining Bayesian + Hu moment with an accuracy rate of up to 90% which could detect normal human sperm heads. It is concluded that the proposed model can detect and classify images containing human sperm head objects. This model can increase accuracy, so it is very appropriate to be applied in the medical field

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References

Aksoy, E., Aktan, T. M., Duman, S., & Cuce, G. (2012). Assessment of Spermatozoa Morphology under Light Microscopy with Different Histologic Stains and Comparison of Morphometric Measurements. International Journal of Morphology, 30(4), 1544–1550. https://doi.org/10.4067/s0717-95022012000400045

Auger, J. (2010). Assessing human sperm morphology: Top models, underdogs or biometrics? Asian Journal of Andrology, 12(1), 36–46. https://doi.org/10.1038/aja.2009.8

Chang, V., Heutte, L., Petitjean, C., Härtel, S., & Hitschfeld, N. (2017). Automatic classification of human sperm head morphology. Computers in Biology and Medicine, 84, 205–216. https://doi.org/10.1016/j.compbiomed.2017.03.029

Di Caprio, G., Ferrara, M. A., Miccio, L., Merola, F., Memmolo, P., Ferraro, P., & Coppola, G. (2015). Holographic imaging of unlabelled sperm cells for semen analysis: A review. Journal of Biophotonics, 8(10), 779–789. https://doi.org/10.1002/jbio.201400093

Flusser, J., Suk, T., & Zitová, B. (2016). 2D and 3D Image Analysis by Moments. 2D and 3D Image Analysis by Moments, 1–529. https://doi.org/10.1002/9781119039402

Ghasemian, F., Mirroshandel, S. A., Monji-Azad, S., Azarnia, M., & Zahiri, Z. (2015). An efficient method for automatic morphological abnormality detection from human sperm images. Computer Methods and Programs in Biomedicine, 122(3), 409–420. https://doi.org/10.1016/j.cmpb.2015.08.013

Gómez, R. A., & Maddison, D. R. (2020). Novelty and emergent patterns in sperm: Morphological diversity and evolution of spermatozoa and sperm conjugation in ground beetles (Coleoptera: Carabidae). Journal of Morphology, 281(8), 862–892. https://doi.org/10.1002/jmor.21144

Iqbal, I., Mustafa, G., & Ma, J. (2020). Deep learning-based morphological classification of human sperm heads. Diagnostics, 10(5). https://doi.org/10.3390/diagnostics10050325

Lammers, J., Splingart, C., Barrière, P., Jean, M., & Fréour, T. (2014). Double-blind prospective study comparing two automated sperm analyzers versus manual semen assessment. Journal of Assisted Reproduction and Genetics, 31(1), 35–43. https://doi.org/10.1007/s10815-013-0139-2

Leung, C., Lu, Z., Esfandiari, N., Casper, R. F., & Sun, Y. (2011). Automated sperm immobilization for intracytoplasmic sperm injection. IEEE Transactions on Biomedical Engineering, 58(4), 935–942. https://doi.org/10.1109/TBME.2010.2098875

Lu, L., Jiang, H., & Wong, W. H. (2013). Multivariate density estimation by Bayesian sequential partitioning. Journal of the American Statistical Association, 108(504), 1402–1410. https://doi.org/10.1080/01621459.2013.813389

Rashid, O., Al-Hamadi, A., & Michaelis, B. (2010). Utilizing invariant descriptors for finger spelling American sign language using SVM. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6453 LNCS(PART 1), 253–263. https://doi.org/10.1007/978-3-642-17289-2_25

Riordon, J., McCallum, C., & Sinton, D. (2019). Deep learning for the classification of human sperm. Computers in Biology and Medicine, 111(June). https://doi.org/10.1016/j.compbiomed.2019.103342

Shaker, F., Monadjemi, S. A., Alirezaie, J., & Naghsh-Nilchi, A. R. (2017). A dictionary learning approach for human sperm heads classification. Computers in Biology and Medicine, 91, 181–190. https://doi.org/10.1016/j.compbiomed.2017.10.009

Shen, W., Tokdar, S. T., & Ghosal, S. (2013). Adaptive Bayesian multivariate density estimation with Dirichlet mixtures. Biometrika, 100(3), 623–640. https://doi.org/10.1093/biomet/ast015

Sun, F., Ko, E., & Martin, R. H. (2006). Is there a relationship between sperm chromosome abnormalities and sperm morphology? Reproductive Biology and Endocrinology, 4(February). https://doi.org/10.1186/1477-7827-4-1

Yadav, R. B., Nishchal, N. K., Gupta, A. K., & Rastogi, V. K. (2008). Retrieval and classification of objects using generic Fourier, Legendre moment, and wavelet Zernike moment descriptors and recognition using joint transform correlator. Optics and Laser Technology, 40(3), 517–527. https://doi.org/10.1016/j.optlastec.2007.08.007

YE Bin, P. J. (2003). Improvement and invariance analysis of pseudo-Zernike moments. Journal of Image and Graphics, 3.

Yoon, S. Y., Jellerette, T., Salicioni, A. M., Hoi, C. L., Yoo, M. S., Coward, K., Parrington, J., Grow, D., Cibelli, J. B., Visconti, P. E., Mager, J., & Fissore, R. A. (2008). Human sperm devoid of PLC, zeta 1 fail to induce Ca2+ release and are unable to initiate the first step of embryo development. Journal of Clinical Investigation, 118(11), 3671–3681. https://doi.org/10.1172/JCI36942

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How to Cite

Zonyfar, C., & Baihaqi, K. A. (2021). Bayesian Pixel Density Estimation Modeling to Detect Human Sperm Sample Image Based on Sperm Head Shape. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 5(2B), 91-99. https://doi.org/10.33395/sinkron.v6i1.11148