Cartesian genetic programming for image analysis of the developing drosophila eye

封面

如何引用文章

全文:

开放存取 开放存取
受限制的访问 ##reader.subscriptionAccessGranted##
受限制的访问 订阅存取

详细

Automatic feature extraction methods have gained increasing attention in modern image processing. The confocal images of the single-layered epithelium of the developing Drosophila eye may form an excellent model system to develop methods for complex feature extraction. The aim of this work was to explore Cartesian genetic programming for determination of the boundaries of ommatidia, the light-sensitive units in the presumptive eye region. Application of Cartesian genetic programming for the analysis of Fasciclin III expression has shown good results. This opens interesting perspectives for further use of this technology in the automatic analysis of confocal images.

作者简介

N. Danilov

Peter the Great St. Petersburg Polytechnic University

St. Petersburg, Russia

K. Kozlov

Peter the Great St. Petersburg Polytechnic University

St. Petersburg, Russia

S. Surkova

Peter the Great St. Petersburg Polytechnic University

Email: surkova_syu@spbstu.ru
St. Petersburg, Russia

M. Samsonova

Peter the Great St. Petersburg Polytechnic University

St. Petersburg, Russia

参考

  1. И. А. Русанова, в сб. матер. Всероссийской школы-семинара (Саратов, 01 октября 2018 г.), под ред. Д. А. Усанова (Изд-во "Саратовский источник", Саратов, 2018), сс. 78-81.
  2. К. Н. Козлов, Е. В. Голубкова, Л. А. Мамон и др., Биофизика, 67, 283 (2022). DOI: 10.31857/ S0006302922020119
  3. J. P. Kumar, Devel. Dynamics, 241, 136 (2012). doi: 10.1002/dvdy.23707
  4. S. Surkova, J. Gorne, S. Nuzhdin, et al., Devel. Biol., 476, 41 (2021). doi: 10.1016/j.ydbio.2021.03.005.
  5. J. Y. Roignant and J. E Treisman, Int. J. Devel. Biol. 53, 795 (2009). doi: 10.1387/ijdb.072483jr
  6. J. E. Treisman, Wiley Interdisc. Rev. Devel. Biol., 2, 545 (2013). doi: 10.1002/wdev.100
  7. S. Ali, S. A. Signor, K. Kozlov, et al., Evolution & Development, 21, 157 (2019). doi: 10.1111/ede.12283
  8. L. Liu, L. Shao and X. Li, Inf. Sci., 316, 567 (2015). doi: 10.1016/j.ins.2014.06.030
  9. A. Lensen, H. Al-Sahaf, M. Zhang, et al., in EuroGP 2016. LNCS, Ed. by M. I. Heywood, J. McDermott, M. Castelli et al. (Springer, Cham, 2016), v. 9594, pp. 51-67. doi: 10.1007/978-3-319-30668-1_4
  10. S.Ruberto, V. Terragni, and J. Moore, in Parallel Problem Solving from Nature. Lecture Notes in Computer Science Image Feature Learning with Genetic Programming (Springer, Cham, 2020), pp. 63-78. doi: 10.1007/978-3-030-58115-2_5
  11. C. B. Perez and G. Olague, Intell. Data Anal., 17, 561 (2013). doi: 10.3233/IDA-130594
  12. W. A. Albukhanajer and J. A. Briffa, IEEE Trans. Cybern., 45, 1757 (2015). doi: 10.1109/TCYB. 2014.2360074
  13. J. F. Miller, P. Thomson, and T.C. Fogarty, in Genetic Algorithms and Evolution Strategies in Engineering and Computer Science: Recent Advancements and Industrial Applications, Ed. by D. Quagliarella, J. Periaux, C. Poloni, and G. Winter (Wiley, 1998), pp. 105-131.
  14. M. A. Kramer, AIChE J. 37, 233 (1991). doi: 10.1002/aic.690370209
  15. A. Makhzani and B. J. Frey, in Advances in Neural Information Processing Systems, Ed. by C. Cortes, N. Lawrence, D. Lee, et al. (2015), pp. 2791-2799
  16. P. Vincent, H. Larochelle, Y. Bengio, et al., in Proc.Int. Conf. on Machine Learning, ICML 2008 (2008). pp. 1096-1103. doi: 10.1145/1390156.1390294
  17. P. M. Snow, A. J. Bieber, and C. S. Goodman, Cell, 59, 313 (1989). doi: 10.1016/0092-8674(89)90293-6
  18. K. Kozlov, A. Pisarev, J. Kaandorp, et al., in Abstr. Bookof the 9th Int. Conf. Syst. Biol. (Goteborg, 2008), p. 191.

版权所有 © Russian Academy of Sciences, 2023

##common.cookie##