تعداد نشریات | 11 |
تعداد شمارهها | 208 |
تعداد مقالات | 2,090 |
تعداد مشاهده مقاله | 2,829,889 |
تعداد دریافت فایل اصل مقاله | 2,049,410 |
A New Method for Sperm Detection in Infertility Cure: Hypothesis Testing Based on Fuzzy Entropy Decision | ||
Journal of Electrical and Computer Engineering Innovations (JECEI) | ||
مقاله 3، دوره 2، شماره 2 - شماره پیاپی 4، مهر 2014، صفحه 69-76 اصل مقاله (3.27 M) | ||
نوع مقاله: Original Research Paper | ||
شناسه دیجیتال (DOI): 10.22061/jecei.2014.244 | ||
نویسندگان | ||
S.V. Shojaedini* 1؛ M. Heydari2 | ||
1Assistant Professor of Institute of Electrical Engineering and Information Technology, Iranian Research Organization for Science and Technology, Tehran, Iran | ||
2Iranian Research Organization for Science and Technology | ||
تاریخ دریافت: 24 اسفند 1392، تاریخ بازنگری: 09 خرداد 1393، تاریخ پذیرش: 01 تیر 1393 | ||
چکیده | ||
In this paper, a new method is introduced for sperm detection in microscopic images for infertility treatment. In this method, firstly a hypothesis testing function is defined to separate sperms from plasma, non-sperm semen particles and noise. Then, some primary candidates are selected for sperms by watershed-based segmentation algorithm. Finally, candidates are either confirmed or rejected using fuzzy entropy decision algorithm. Performance of the proposed method is evaluated on real captured images containing sperms and other specimens of semen in two different scenarios. In the first scenario, semen has low density of sperms however the second scenario belongs to semen with high density of sperms. The obtained results show the greater ability of the proposed method in sperm detection compared to present approaches in both of scenarios. Furthermore, it is shown that 8% and 15% improvements in sperm detection in the first and second scenarios can be achieved by the proposed algorithm. As the final results, the proposed algorithm not only doesn't lead to extract more false objects but also decrease the rate of false detections are decreased compared to existing algorithms. | ||
کلیدواژهها | ||
Sperm detection؛ Microscopic image؛ Hypothesis testing؛ Fuzzy entropy decision | ||
مراجع | ||
[1] Sproff, L., and Fritz, M., Clinical Gynecologic Endocrinology and Infertility, 7th Edition, Williams & Wilkins, Baltimore, USA, 2004.
[2] The UK Department of Health report for guidance and information on commissioning fertility services, Regulated Fertility Services: A commissioning aid, Department of Health, UK, June 2009.
[3] Menkveld, R., “Semen parameters including WHO and strict criteria morphology in a fertile and infertile population: an effort towards standardization of In-Vivo thresholds,” Human Reproduction., VOL 16, No 6, 2001, pp. 1165-71.
[4] Shi, L., Nascimento, J., Chandsawangbhuwana, C., Bernz, M., and Botvinick, E., “Real-Time Automated Tracking and Trapping System for Sperm,” Microscopy Research And Technique, VOL 69, 2006, pp. 894-902.
[5] Menkveld, R., “Reϐlection of CASA after 25 years,” Journal of Andrology., VOL 25, No 3, 2004, pp. 317-325.
[6] Menkveld, R., and Kruger, T., Evaluation of sperm morphology by light microscopy, Assisted Reproduction, 2nd Edition, Partheon Publishing Group, 1996.
[7] Gutierrez, R., “Use of the Sperm-Class Analyzer for objective assessment of human sperm morphology,” International Journal of Andrology., VOL 26, No 5, 2003, pp. 262-270.
[8] Leung, C., Lu, Z., and Casper, F., “Detection and Tracking of Low Contrast Human Sperm Tail,” Proceedings of annual IEEE Conference on Automation Science and Engineering, Toronto., Canada, 2010, pp. 264-268.
[9] Carrillo, H., Villarreal, J., Sotaquir, M., Goelkel, A., and Gutirrez, R., “A Computer aided tool for the assessment of human sperm morphology,” Proceedings of the 7th International Conference on Bioinformatics and Bioengineering., Boston., USA, 2007, pp. 1152-1157.
[10] Leung, C., Lu, Z., Esfandiari, N., Casper, R., and Sun, Y., “Automated Sperm Immobilization for Intracytoplasmic Sperm Injection,” IEEE Transactions on Biomedical Engineering, VOL 58, No 4, 2011, pp. 935-942.
[11] Shi, L., and Nascimento, M., “An automatic system to study sperm motility and energetics,” Biomedical Microdevices., VOL 10, No 4, 2008, pp. 573-583.
[12] Hassanpour, H., and Yousefian, H., “An improved pixonbased approach for image segmentation,” International Journal of Engineering, Transactions A: Basics, VOL 24, No 1, 2011, pp. 25-35.
[13] Zheng, L., and Wang, Y., “The sperm video segmentation based on dynamic threshold,” Proceedings of the Ninth International Conference on Machine Learning and Cybernetics., Qingdao., China, 2010, pp. 2444-48.
[14] Abbiramy, S., and Shanthi, V., “Spermatozoa Segmentation and Morphological Parameter Analysis Based Detection of Teratozoospermia,” International Journal of Computer Applications, VOL 3, No 7, 2010, pp. 19-23.
[15] Alias, F., Isa, N., Sulaiman, S., and Zamli, K., “Detection of Sprague Dawley Sperm Using Matching Method,” Lecture Notes in Computer Science., KES3,VOL 5179 Springer, 2008, pp. 541-547.
[16] Roerdink, J., and Meijster, A., “watershed transform: definitions, algorithms, and parallelization strategies,” FundamentaInformaticae, VOL 41, 2000, pp. 187-228.
[17] Bieniek, A., and Moga, A., “An efficient watershed algorithm based on connected components,” Pattern Recognition., VOL 33, 2000, pp. 907-916.
[18] Malik, J., Belongie, S., Leung, T., and Shi, J., “Contour and texture analysis for image segmentation,” International Journal of Computer Vision., VOL 43, No 1, 2001, pp. 7-27.
[19] Buf, D., Kardan, M., and Spann, M., “Texture feature performance for image segmentation,” Pattern Recognition., VOL 3, No 3, 1990, pp. 291-309.
[20] Al-Sharhan, S., “Fuzzy entropy: a brief survey,” 10th IEEE International Conference on Fuzzy Systems, Melbourne., Australia, 2001, pp. 1135-39.
[21] Xuan, S., Xiaoye, W., Zhou, W., and Ying, X., “A new fuzzy clustering algorithm based on entropy weighting,” Journal of Computational Information Systems, VOL 6, No 10, 2010, pp. 3319-26 | ||
آمار تعداد مشاهده مقاله: 2,585 تعداد دریافت فایل اصل مقاله: 1,945 |