Automatic detection of embryo using Particle Swarm Optimization based Hough Transform

In-Vitro Fertilization (IVF) is a procedure to obtain embryo by inseminating oocyte and sperm outside human body. Several embryos are produced at the end of this procedure and it remains a problem to select the most appropriate embryo to be implanted into uterus. Many strategies have been proposed for selection of the embryo. The latest is time-lapse microscopy which monitors the embryo development continuously. An automatic method using computer to detect and locate the position of the embryo is thus needed. In this paper, an approach based on a modification of Hough Transform using Particle Swarm Optimization (PSO) is proposed to approximate the embryo as a circle. Each PSO particle represents a circle in the parameter space and mainly used to reduce the computational complexity of Hough Transform. Experiment result showed that the proposed method is able to detect the position of the embryo accurately. The result from this method can be used to extract criteria for embryo transfer purpose.

[1]  Danny Crookes,et al.  Live-Cell Tracking Using SIFT Features in DIC Microscopic Videos , 2010, IEEE Transactions on Biomedical Engineering.

[2]  L. Rienzi,et al.  Significance of morphological attributes of the early embryo. , 2005, Reproductive biomedicine online.

[3]  S. Chamayou,et al.  The use of morphokinetic parameters to select all embryos with full capacity to implant , 2013, Journal of Assisted Reproduction and Genetics.

[4]  Susan Pickering,et al.  Early embryo development is an indicator of implantation potential. , 2004, Reproductive biomedicine online.

[5]  Pedro Larrañaga,et al.  Selection of human embryos for transfer by Bayesian classifiers , 2008, Comput. Biol. Medicine.

[6]  Erkki Oja,et al.  Randomized Hough transform (RHT) , 1990, [1990] Proceedings. 10th International Conference on Pattern Recognition.

[7]  Carlo Flamigni,et al.  Predictive factors for embryo implantation potential. , 2005, Reproductive biomedicine online.

[8]  Qiang Ji,et al.  A new efficient ellipse detection method , 2002, Object recognition supported by user interaction for service robots.

[9]  Fumihito Arai,et al.  OCIAN; On-chip impedance analyzer for measurement of cellular mechanical parameters , 2012, 2012 International Symposium on Micro-NanoMechatronics and Human Science (MHS).

[10]  M. Meseguer,et al.  The use of morphokinetics as a predictor of embryo implantation. , 2011, Human reproduction.

[11]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[12]  Yann LeCun,et al.  Toward automatic phenotyping of developing embryos from videos , 2005, IEEE Transactions on Image Processing.

[13]  Loris Nanni,et al.  Artificial intelligence techniques for embryo and oocyte classification. , 2013, Reproductive biomedicine online.

[14]  J. Tesarik,et al.  Pronuclear morphology predicts embryo development and chromosome constitution. , 2004, Reproductive biomedicine online.

[15]  Jan Gerris,et al.  Single embryo transfer - state of the art. , 2003, Reproductive biomedicine online.