Sperm Cells Segmentation in Micrographic Images Through Lambertian Reflectance Model
Nowadays, male infertility has increased worldwide. Therefore, a rigorous analysis of sperm cells is required to diagnose this problem. Currently, this analysis is performed based on the expert opinion. In order to support the experts in fertility diagnosis, several image processing techniques have been proposed. In this paper, we present an approach that combines the Lambertian model based on surface reflectance with mathematical morphology for sperm cells segmentation in micrographic images. We have applied our approach to a set of 73 images. The results of our approach have been evaluated based on ground truth segmentations and similarity indices, finding a high correlation between our results and manual segmentation.
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- 2.Bijar, A., Benavent, A.P., Mikaeili, M., Khayati, R.: Fully automatic identification and discrimination of sperm’s parts in microscopic images of stained human semen smear. Journal of Biomedical Science & Engineering (2012)Google Scholar
- 3.Carrillo, H., Villarreal, J., Sotaquira, M., Goelkel, M., Gutierrez, R.: A computer aided tool for the assessment of human sperm morphology. In: Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2007, pp. 1152–1157. IEEE (2007)Google Scholar
- 5.Dai Qingyun, Y.Y.: The advanced of mathematieal morphology in image processing. Control Theory and Applications 18(04), 479–481 (2001)Google Scholar
- 6.Horprasert, T., Harwood, D., Davis, L.S.: A robust background subtraction and shadow detection. In: Proc. ACCV, pP. 983–988 (2000)Google Scholar
- 10.Soler, C., García-Molina, A., Sancho, M., Contell, J., Núñez, M., Cooper, T.G.: A new technique for analysis of human sperm morphology in unstained cells from raw semen. Reproduction, Fertility and Development (2014)Google Scholar
- 11.Tomlinson, M.J., Pooley, K., Simpson, T., Newton, T., Hopkisson, J., Jayaprakasan, K., Jayaprakasan, R., Naeem, A., Pridmore, T.: Validation of a novel computer-assisted sperm analysis (casa) system using multitarget-tracking algorithms. Fertility and Sterility 3(6), 1911–1920 (2010)CrossRefGoogle Scholar
- 12.Unnikrishnan R., Hebert, M.: Measures of similarity. In: Seventh IEEE Workshops on Application of Computer Vision, WACV/MOTIONS 2005, vol. 1 (2005)Google Scholar
- 13.Wang, Y., Jia, Y., Yuchi, M., Ding, M.: The computer-assisted sperm analysis (casa) technique for sperm morphology evaluation. In: 2011 International Conference on Intelligent Computation and Bio-Medical Instrumentation (ICBMI), pp. 279–282, December 2011Google Scholar