Detecting Ellipses in Embryo Images Using Arc Detection Method with Particle Swarm for Blastomere-Quality Measurement System
Aprinaldi Jasa Mantau*, Anom Bowolaksono**, Budi Wiweko***, and Wisnu Jatmiko*
*Faculty of Computer Science, Universitas Indonesia
Depok, Jawa Barat, Indonesia
**Faculty of Mathematics and Natural Science, Universitas Indonesia
Depok, Jawa Barat, Indonesia
***Faculty of Medicine, Universitas Indonesia
The objective of this paper is to present a novel method, based on a swarm intelligence algorithm, for ellipse detection in digital images of embryo. The process is carried out in several stages. First, edge detection is performed on the image. Then, line segments in the image are detected, and potential elliptical arc segments are extracted from the line segments. Afterward, the detection process is carried out using the Particle Swarm Optimization (PSO) method, which utilize the calculation of the fitness function from the arc segment previously detected. The PSO technique, which is the idea behind the proposed algorithm, is used to find the actual ellipses by combining potential elliptical arcs. The best combination of potential arcs is determined by means a voting technique that utilizes three important points on the arc, namely the starting point, midpoint, and endpoint, so the voting is more efficient than doing the voting for every single pixel in the image. Furthermore, this method is used an embryo image that has following the characteristics: multiple ellipses, a lot of noise, an incomplete ellipse, low image contrast, and overlapping cells. Experiment show that the proposed method detects the ellipses better than do several voting-based ellipse detection methods such as RHT, IRHT, and PSORHT. On the other hand, the experiments show that the proposed method has a higher average hit rate than do other methods. This research is used to increase the success rate of In-Vitro Fertilization (IVF).
-  T. Baczkowski, R Kurzawa, and W. Glabowski, “Methods of embryo scoring in in vitro fertilization,” Reproductive biology, Vol.4, No.1, pp. 3-22, 2004.
-  E. S. Filho, “A Review on Automatic Analysis of Human Embryo Microscope Images,” The Open Biomedical Engineering J., No.4, pp. 170-177, 2010.
-  L. A. Scott and S. Smith, “The successful use of pronuclear embryo transfers the day following oocyte retrieval,” Hum. Reprod, Vol.13, No.4, pp. 1003-1013, 1998.
-  D. A. Moralesa, et al., “Bayesian classification for the selection of in vitro human embryos using morphological and clinical data,” Computer Methods and Programs in Biomedicine, Vol.90, No.2, pp. 104-116, 2007.
-  C. Manna, L. Nanni, A. Lumini, S. Pappalardo, “Artificial intelligence techniques for embryo and oocyte classification,” Reproductive Biomedicine Online, Vol.26, No.1, pp. 42-49, 2013.
-  C. Manna, G. Patrizi, A. Rahman, H. Sallam, “Experimental results on the recognition of embryos in human assisted reproduction,” Reproductive Biomedicine Online, Vol.8, No.4, pp. 460-469, 2004.
-  W. Lu and J. Tan, “Detection of incomplete ellipse in images with strong noise by iterative randomized Hough transform (IRHT),” Pattern Recognition, Vol.41, pp. 1268-1279, 2008.
-  H. D. Cheng, Y. Guo, and Y. Zhang, “A novel Hough transform based on eliminating particle swarm optimization and its applications,” Pattern Recognition, Vol.42, pp. 1959-1969, 2009.
-  I. P. Satwika, I. Habibie, M. A. Ma’sum, A. Febrian, and E. Budianto, “Particle swarm optimation based 2-dimensional randomized hough transform for fetal head biometry detection and approximation in ultrasound imaging,” Advanced Computer Science and Information Systems (ICACSIS), 2014 Int. Conf. on, pp. 468-473, 18-19 Oct. 2014.
-  Cuneyt Akinlar and Cihan Topal, “EDCircles: A real-time circle detector with a false detection control,” Pattern Recognition, Vol.46, pp. 725-740, 2012.
-  J. Van Blerkom, H. Bell, and D. Weipz, “Cellular and developmental biological aspects of bovine meiotic maturation, fertilization, and preimplantation embryogenesis in vitro,” J. of electron microscopy technique, Vol.16, No.4, pp. 298-323, 1990.
-  L. Scott, et al., “The morphology of human pronuclear embryos is positively related to blastocyst development and implantation,” Human Reproduction, Vol.15, No.11, pp. 2394-2403, 2000.
-  L. Xu, E. Oja, and P. Kultanen, “A net Cure detection method: Randomized hough transform (RHT),” Patern Regognition Letters, Vol.11, No.5, pp. 331-339, 1990.
-  J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” Proc. of IEEE Int. Conf. on Neural Network, 1995.
-  J. Kennedy, R. C. Eberhart, and M. Kaufman, “Swarm Intelligence,” Morgan Kaufman, San Fransisco, 2001.
-  J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” Proc. of IEEE Int. Conf. on Neural Network, Vol.4, pp. 1942-1948, 1995.
-  W. Jatmiko, Rochmatullah, B. Kusumoputro, K. Sekiyama, and T. Fukuda, “Fuzzy learning vector quantization based on particle swarm optimization for artificial odor dicrimination system,” WSEAS Trans. on Systems, Vol.8, No.12, pp. 1239-1252, 2009.
-  W. Jatmiko, A. Nugraha, R. Effendi, W. Pambuko, R. Mardian, K. Sekiyama, and T. Fukuda, “Localizing Multiple odor Sources in a dynamic environment based on Modified niche Particle Swarm Optimization with flow of wind,” WSEAS Trans. on Systems, Vol.8, No.11, pp. 1187-1196, 2009.
-  W. Jatmiko, K. Sekiyama, and T. Fukuda, “A particle swarm-based mobile sensor network for odor source localization in a dynamic environment,” Distributed Autonomous Robotic Systems, Vol.7, pp. 71-80, 2006.
-  Aprinaldi, G. Jati, A. A. S. Gunawan, A. Bowolaksono, S. W. Lestari, and W. Jatmiko, “Human Sperm tracking using Particle Swarm Optimization combined with Smoothing Stochastic sampling on low frame rate video,” 2015 Int. Symposium on Micro-NanoMechatronics and Human Science (MHS), 2015.
-  C. Akinlar and C. Topal, “EDPF: A Real-time Parameter-free Edge Segment Detector with a False Detection Control,” Int. J. of Pattern Recognition and Artificial Intelligence, Vol.26, No.1, 2012.
-  R. G. von Gioi, J. Jakubowicz, J. M. Morel, and G. Randall, “LSD: A Fast Line Segment Detector with a False Detection Control,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.32, No.4, pp. 722-732, 2010.
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