APPLICATION OF NUMERICAL INTELLIGENCE METHODS FOR THE AUTOMATIC QUALITY GRADING OF AN EMBRYO DEVELOPMENT
DOI:
https://doi.org/10.47839/ijc.15.3.850Keywords:
In vitro fertilization, computer vision, classification.Abstract
In vitro fertilization – a procedure which aims to get the embryo to adapt the methods of "oocyte" fertilized sperm outside the human body. At the end of this procedure there are several embryos. This paper represents overview of tracking-free and tracking-based methods for detection of important embryo development stages. Tracking-based method represents well known classical object tracking techniques. For tracking-free method were selected statistical feature extraction techniques and classification methods: Classification with training and classification without training. For the feature extraction proposed statistical methods: entropy, invariant moments and principal components analyses. For the classification are used neural networks, support vector machine and K-nearest neighbor method. Data collected consist of 500 images for each class. 70 percent of images are dedicated for training, and 30 percent for testing. The proposed method is checked by experiment. It is expected that this method will work well in video sequences.References
Jonaitis D., Raudonis V., “Automatic detection and tracking of embryo development,” in Proceedings of the Conference on Biomedical Engineering, Kaunas, 2014, pp. 139-143.
C. C. Wong, K. Loewke, N. Bossert, B. Behr, C. J. De Jonge, T. Baer, R. Reijo Pera, “Non-invasive imaging of human embryos before embryonic genome activation predicts development to the blastocyst stage,” Nature Biotechnology, Vol. 28, Issue 10, pp. 1115-1121, 2010.
Wharf E., Dimitrakopoulos A., Khalaf Y., Pickering S., “Early embryo development is an indicator of implantation potential,” Reproductive Biomedicine Online, Vol. 8, No. 2, pp. 212-218, 2004.
K. C. Chen, N. Bouguila, and D. Ziou, “Quantization-free parameter space reduction in ellipse detection,” Expert Syst. Appl., Vol. 38, No. 6, pp. 7622-7632, 2011.
H. Jiandong, “Ellipse detection based on principal components analysis,” in Proceedings of the International Conference on Computer Application and system Modeling, 2010, pp. 258-261.
J. K. Lee, B. A. Wood and T. S. Newman, “Very fats ellipse detection using GPU-based RHT,” in Proceeding of the 19th IEEE International Conference on Pattern Recognition (ICPR’20008), Tampa, USA, 8-10 December 2008, pp. 1-4.
W. D. Prasad and M. Leung, “Clustering of ellipses based on their distinctiveness: An aid to ellipse detection algorithm,” in Proceedings of the 3rd IEEE International Conference (ICCSIT’2010), Chenda, China, 9-10 July 2010, Vol. 8, pp. 292-297.
T. Cooke, “A fast automatic ellipse detection,” in Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (Dicta’2010), Sydney, Australia, 1-3 December 2010, pp. 575-580.
http://www.resolve.org/about/fast-facts-about-fertility.html. [Accessed 2016-05-10].
Hu M. K., “Visual Pattern Recognition by Moment Invariants,” IRE Trans. Info. Theory, Vol. IT–8, pp. 179-187, 1962.
Jolliffe I. T., Principal Component Analysis, Series: Springer, 2nd ed., Springer, NY, 2002, XXIX, 487 p.
Fukushima K., “Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position,” Biological Cybernetics, Vol. 36, Issue 4, pp. 93-202, 1980.
Coomans D., Massart D. L., “Alternative k-nearest neighbour rules in supervised pattern recognition: Part 1. k-Nearest neighbour classification by using alternative voting rules,” Analytica Chimica Acta, Vol. 136, pp. 15-27, 1982.
Cortes C., Vapnik V. “Support-vector networks,” Machine Learning, Vol. 20, Issue 3, pp. 273-297, 1995.
D. Jonaitis, V. Raudonis, A. Lipnickas, “Application of computer vision methods in automatic analysis of embryo development,” in Proceedings of the 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS’2015), Warsaw, Poland, 24-26 September 2015, Vol. 1, pp. 257-260.
Downloads
Published
How to Cite
Issue
Section
License
International Journal of Computing is an open access journal. Authors who publish with this journal agree to the following terms:• Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
• Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
• Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
