[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

CSA-DE/EDA: a Novel Bio-inspired Algorithm for Function Optimization and Segmentation of Brain MR Images

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

The clonal selection algorithm (CSA), which describes the basic features of an immune response to an antigenic stimulus, has drawn a lot of attention in the biologically inspired computing community, due to its highly adaptive and easy-to-implement nature. Despite many successful applications, CSA still suffers from limited ability to explore the solution space. In this paper, we incorporate the differential evolution (DE) algorithm and the estimation of distribution algorithm (EDA) into CSA, and thus propose a novel bio-inspired algorithm referred to as CSA-DE/EDA. In the proposed algorithm, the hypermutation and receptor editing processes are implemented based on DE and EDA, which provide improved local and global search ability, respectively. We have applied the proposed algorithm to five commonly used benchmark functions for optimization and brain magnetic resonance (MR) image segmentation. Our comparative experimental results show that the proposed CSA-DE/EDA algorithm outperforms several bio-inspired computing techniques. CSA-DE/EDA is a compelling bio-inspired algorithm for optimization tasks.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Molina D, LaTorre A, Herrera F. An insight into bio-inspired and evolutionary algorithms for global optimization: review, analysis, and lessons learnt over a decade of competitions. Cogn Comput. 2018;10(4):517–44. https://doi.org/10.1007/s12559-018-9554-0.

    Article  Google Scholar 

  2. Siddique N, Adeli H. Nature inspired computing: an overview and some future directions. Cogn Comput. 2015;7(6):706–14. https://doi.org/10.1007/s12559-015-9370-8.

    Article  Google Scholar 

  3. Srinivas M, Patnaik LM. Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans Syst Man Cybern. 1994;24(4):656–67. https://doi.org/10.1109/21.286385.

    Article  Google Scholar 

  4. Haktanirlar Ulutas B, Kulturel-Konak S. A review of clonal selection algorithm and its applications. Artif Intell Rev. 2011;36(2):117–38. https://doi.org/10.1007/s10462-011-9206-1.

    Article  Google Scholar 

  5. Binitha S, Sathya SS. A survey of bio inspired optimization algorithms. Int J Soft Comput Eng. 2012;2(2):137–51.

    Google Scholar 

  6. Ma X, Li X, Zhang Q, Tang K, Liang Z, Xie W, et al. A survey on cooperative co-evolutionary algorithms. IEEE Trans Evol Comput. 2018;23:421–41. https://doi.org/10.1109/TEVC.2018.2868770.

    Article  Google Scholar 

  7. Gupta A, Ong Y-S. Back to the roots: multi-X evolutionary computation. Cogn Comput. 2019;11:1–17. https://doi.org/10.1007/s12559-018-9620-7.

    Article  CAS  Google Scholar 

  8. Tang Q, Shen Y, Hu C, Zeng J, Gong W. Swarm intelligence: based cooperation optimization of multi-modal functions. Cogn Comput. 2013;5(1):48–55. https://doi.org/10.1007/s12559-012-9144-5.

    Article  Google Scholar 

  9. Horn J, Nafpliotis N, Goldberg DE, editors. A niched Pareto genetic algorithm for multiobjective optimization. In Proceedings of the first IEEE conference on evolutionary computation, IEEE World Congress on Computational Intelligence; 1994 Jun. Vol. 1, p. 82–87.

  10. Zhang Q, Sun J, Tsang E, Ford J. Hybrid estimation of distribution algorithm for global optimization. Eng Comput. 2004;21(1):91–107.

    Article  Google Scholar 

  11. Wang SY, Wang L. An estimation of distribution algorithm-based memetic algorithm for the distributed assembly permutation flow-shop scheduling problem. IEEE Trans Syst Man Cybern Syst Hum. 2015;46(1):139–49.

    Article  Google Scholar 

  12. Wu CG, Wang L, Zheng XL. An effective estimation of distribution algorithm for solving uniform parallel machine scheduling problem with precedence constraints. IEEE Congress on Evolutionary Computation; 2016 Jul 24. p. 2626–32.

  13. Sun JY, Zhang QF, Tsang EPK. DE/EDA: a new evolutionary algorithm for global optimization. Inf Sci. 2005;169(3–4):249–62. https://doi.org/10.1016/j.ins.2004.06.009.

    Article  Google Scholar 

  14. Price KV. Differential evolution vs. the functions of the 2nd ICEO. In Proceedings of 1997 IEEE International Conference on Evolutionary Computation; 1997 Apr 13. p. 153–157.

  15. Tang L, Dong Y, Liu J. Differential evolution with an individual-dependent mechanism. IEEE Trans Evol Comput. 2014;19(4):560–74.

    Article  Google Scholar 

  16. Kim SS, Mcloone S, Byeon JH, Lee S, Liu H. Cognitively inspired artificial bee colony clustering for cognitive wireless sensor networks. Cogn Comput. 2017;9(2):207–24.

    Article  Google Scholar 

  17. Xiaoyang F, Shuqing Z. An improved artificial immune recognition system based on the average scatter matrix trace criterion. Adv Swarm Intell. 2012;14(1):284–90.

    Google Scholar 

  18. Vidal JM, Orozco ALS, Villalba LJG. Adaptive artificial immune networks for mitigating DoS flooding attacks. Swarm Evol Comput. 2018;38:94–108.

    Article  Google Scholar 

  19. Zhang X, Zhang E, Li R. Optimized feature extraction by immune clonal selection algorithm. IEEE Congress on Evolutionary Computation; 2012 Jun 10. p. 1–6.

  20. Banerjee S, Moses M. Scale invariance of immune system response rates and times: perspectives on immune system architecture and implications for artificial immune systems. Swarm Intell. 2010;4(4):301–18.

    Article  Google Scholar 

  21. LNd C, Zuben FJV. Learning and optimization using the clonal selection principle. IEEE Trans Evol Comput. 2002;6(3):239–51.

    Article  Google Scholar 

  22. Zhang L, Gong M, Jiao L, Yang J.  Optimal approximation of linear systems by an improved clonal selection algorithm. 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence); 2008 Jun 1. p. 527–534.

  23. Batista L, Guimaraes FG, Ramirez JA. A distributed clonal selection algorithm for optimization in electromagnetics. IEEE Trans Magn. 2009;45(3):1598–601. https://doi.org/10.1109/tmag.2009.2012752.

    Article  Google Scholar 

  24. Ulutas BH, Kulturel-Konak S. A review of clonal selection algorithm and its applications. Artif Intell Rev. 2011;36(2):117–38.

    Article  Google Scholar 

  25. Gong M, Jiao L, Zhang L. Baldwinian learning in clonal selection algorithm for optimization. Inf Sci. 2010;180(8):1218–36.

    Article  Google Scholar 

  26. Xu N, Ding Y, Ren L, Hao K. Degeneration recognizing clonal selection algorithm for multimodal optimization. IEEE Trans Cybern. 2018;48(3):848–61. https://doi.org/10.1109/TCYB.2017.2657797.

    Article  PubMed  Google Scholar 

  27. Das S, Mullick SS, Suganthan PN. Recent advances in differential evolution—an updated survey. Swarm Evol Comput. 2016;27:1–30.

    Article  Google Scholar 

  28. Bersini H, Dorigo M, Langerman S, Seront G, Gambardella L. Results of the first international contest on evolutionary optimisation (1st ICEO). In Proceedings of IEEE International Conference on Evolutionary Computation; 1996 May 20. p. 611–615.

  29. Efrñn MM, Coello CAC. A comparative study of differential evolution variants for global optimization. Genetic and Evolutionary Computation Conference; 2006. p. 485–92.

    Google Scholar 

  30. Li G, Chan TM, Leung KS, Lee KH. An estimation of distribution algorithm for motif discovery. IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence); 2008 Jun 1. p. 2411–8.

  31. Chen T, Tang K, Chen G, Yao X. On the analysis of average time complexity of estimation of distribution algorithms. IEEE Congress on Evolutionary Computation; 2007 Sep 25. p. 453–460.

  32. Larrañaga P, Lozano JA. Estimation of distribution algorithms: a new tool for evolutionary computation: Springer Science & Business Media; 2001 Oct 31.

  33. Campelo F, Guimaraes FG, Igarashi H, Ramirez JA. A clonal selection algorithm for optimization in electromagnetics. IEEE Trans Magn. 2005;41(5):1736–9. https://doi.org/10.1109/TMAG.2005.846043.

    Article  Google Scholar 

  34. Xie Y, Xia Y, Zhang J, Song Y, Feng D, Fulham M, et al. Knowledge-based collaborative deep learning for benign-malignant lung nodule classification on chest ct. IEEE Trans Med Imaging. 2018;38:991–1004. https://doi.org/10.1109/TMI.2018.2876510.

    Article  PubMed  Google Scholar 

  35. Zhang J, Xie Y, Xia Y, Shen C. Attention residual learning for skin lesion classification. IEEE Trans Med Imaging. 2019 Jan 21. https://doi.org/10.1109/TMI.2019.2893944.

    Article  PubMed  Google Scholar 

  36. Song Y, Tan EL, Jiang X, Cheng JZ, Ni D, Chen S, et al. Accurate cervical cell segmentation from overlapping clumps in pap smear images. IEEE Trans Med Imaging. 2016;36(1):288–300.

    Article  PubMed  Google Scholar 

  37. Lei B, Yang P, Wang T, Chen S, Ni D. Relational-regularized discriminative sparse learning for Alzheimer’s disease diagnosis. IEEE Trans Cybern. 2017;47(4):1102–13.

    Article  PubMed  Google Scholar 

  38. Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging. 2001;20(1):45–57. https://doi.org/10.1109/42.906424.

    Article  CAS  PubMed  Google Scholar 

  39. Li C, Gatenby C, Li W, Gore JC. A robust parametric method for bias field estimation and segmentation of MR images. IEEE Conference on Computer Vision and Pattern Recognition. 2009 Jun 20: 218–223.

  40. Besag J. On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society: Series B (Methodological). 1986 Jul; 48(3): 259–279.

    Google Scholar 

  41. Zheng C, Hu Y, Wang L, Qin Q. Region-based MRF model with optimized initial regions for image segmentation. International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE); 2011 Jun 24. p. 3354–7.

  42. Levinshtein A, Stere A, Kutulakos KN, Fleet DJ, Dickinson SJ, Siddiqi K. TurboPixels: fast superpixels using geometric flows. IEEE Trans Pattern Anal Mach Intell. 2009;31(12):2290–7. https://doi.org/10.1109/TPAMI.2009.96.

    Article  PubMed  Google Scholar 

  43. Modestino JW, Zhang J, editors. A Markov random field model-based approach to image interpretation. Computer Vision and Pattern Recognition; 1989. p. 4–8.

    Google Scholar 

  44. Iglesias JE, Liu CY, Thompson PM, Tu Z. Robust brain extraction across datasets and comparison with publicly available methods. IEEE transactions on medical imaging. 2011 Apr 5;30(9):1617–34.

    Article  PubMed  Google Scholar 

  45. Bharatha A, Hirose M, Hata N, Warfield SK, Ferrant M, Zou KH, et al. Evaluation of three-dimensional finite element-based deformable registration of pre- and intraoperative prostate imaging. Med Phys. 2001;28(12):2551–60.

    Article  CAS  PubMed  Google Scholar 

  46. Rohlfing T. Image similarity and tissue overlaps as surrogates for image registration accuracy: widely used but unreliable. IEEE Trans Med Imaging. 2012;31(2):153–63. https://doi.org/10.1109/TMI.2011.2163944.

    Article  PubMed  Google Scholar 

  47. Flandin G, Friston KJ. Statistical parametric mapping (SPM). Scholarpedia. 2008;3(4):6232.

    Article  Google Scholar 

  48. Library FS. in http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/. Accessed Jun 2016.

  49. Tohka J, Krestyannikov E, Dinov ID, Graham A, Shattuck DW, Ruotsalainen U, et al. Genetic algorithms for finite mixture model based voxel classification in neuroimaging. IEEE Trans Med Imaging. 2007;26(5):696–711.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Zhang T, Xia Y, Feng DD. A deformable cosegmentation algorithm for brain MR images. 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012 Aug 28. p. 3215–8.

  51. Zhang T, Xia Y, Feng DD. Hidden Markov random field model based brain MR image segmentation using clonal selection algorithm and Markov chain Monte Carlo method. Biomed Signal Process Control. 2014;12:10–8.

    Article  Google Scholar 

  52. Talairach J, Tournoux P. Co-planar stereotaxic atlas of the human brain. New York: Thieme; 1988.

    Google Scholar 

  53. Filipek PA, Kennedy DN, Caviness VS. Volumetric analysis of central nervous system neoplasm based on MRI. Pediatr Neurol. 1991;7(5):347–51.

    Article  CAS  PubMed  Google Scholar 

  54. Van Leemput K, Maes F, Vandermeulen D, Suetens P. A unifying framework for partial volume segmentation of brain MR images. IEEE Trans Med Imaging. 2003;22(1):105–19.

    Article  PubMed  Google Scholar 

  55. Bishop CM. Pattern recognition and machine learning. New York: Springer Stat Sci. 2006.

  56. Li Z, Xia Y, Ji Z, Zhang Y. Brain voxel classification in magnetic resonance images using niche differential evolution based Bayesian inference of variational mixture of Gaussians. Neurocomputing. 2017;269:47–57.

    Article  Google Scholar 

Download references

Acknowledgements

We appreciate the efforts devoted by the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) to collect and share the clinical MR brain data sets and their manual segmentations for comparing interactive and (semi)-automatic segmentation algorithms for MRI of major brain tissues.

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 61771397, in part by the Science and Technology Innovation Committee of Shenzhen Municipality, China, under Grant JCYJ20180306171334997, in part by the Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University (NPU) under Grant ZZ2019029, in part by Synergy Innovation Foundation of the University and Enterprise for Graduate Students in NPU under Grant XQ201911, and in part by the the Project for Graduate Innovation team of NPU.

Author information

Authors and Affiliations

  1. National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, 710072, China

    Zhe Li & Yong Xia

  2. Research & Development Institute, Northwestern Polytechnical University in Shenzhen, Shenzhen, 518057, China

    Yong Xia & Hichem Sahli

  3. Audio Visual Signal Processing (AVSP), Department of Electronics & Informatics (ETRO), Vrije Universiteit Brussel (VUB), VUB-ETRO, Pleinlaan, 2, B-1050, Brussels, Belgium

    Hichem Sahli

  4. Interuniversity Microelectronics Center, Kapeldreef 75, 3001, Leuven, Belgium

    Hichem Sahli

Authors
  1. Zhe Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yong Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hichem Sahli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yong Xia.

Ethics declarations

Conflict of Interest

We would like to submit a manuscript entitled “CSA-DE/EDA: A Novel Bio-inspired Algorithm for Function Optimization and Segmentation of Brain MR Images” for possible publication in Cognitive Computation. There are no potential conflicts of interest to report.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Z., Xia, Y. & Sahli, H. CSA-DE/EDA: a Novel Bio-inspired Algorithm for Function Optimization and Segmentation of Brain MR Images. Cogn Comput 11, 855–868 (2019). https://doi.org/10.1007/s12559-019-09663-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-019-09663-x

Keywords

Search

Navigation