Swarm Intelligence Approach to 3D Medical Image Segmentation

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 471)

Abstract

In this paper we present a new idea for 3D medical image segmentation based on swarm intelligence and ant colony optimization. The methodology combines selected mechanisms running both mentioned artificial intelligence techniques, e.g. fitness-controlled motion of virtual agents or stigmergy. Foundations of the algorithm are described along with its implementation specification, simulations, results and their analysis also in terms of clarifying the parameterization. Several parameters are introduced and verified in terms of their influence on the method performance. The experiments rely on the segmentation of spleen in computed tomography studies. We also formulate some conclusions on possible ways for the algorithm future development.

Keywords

Image segmentation Swarm intelligence Ant colony optimization 

References

  1. 1.
    Badura, P., Pietka, E.: 3D fuzzy liver tumor segmentation. Inf. Technol. Biomed. Lect. Notes Bioinform. 7339, 47–57 (2012)Google Scholar
  2. 2.
    Badura, P., Pietka, E.: Semi-automatic seed points selection in fuzzy connectedness approach to image segmentation. Comput. Recogn. Syst. Adv. Intell. Soft Comput. 45(2), 679–686 (2007)CrossRefGoogle Scholar
  3. 3.
    Blum, C.: Ant colony optimization: introduction and recent trends. Phys. Life Rev. 2(4), 353–373 (2005)Google Scholar
  4. 4.
    Blum, C.: Beam-ACO-hybridizing ant colony optimization with beam search: an application to open shop scheduling. Comput. Oper. Res. 32, 1565–1591 (2005)CrossRefMATHGoogle Scholar
  5. 5.
    Czajkowska, J., Badura, P., Pietka, E.: 4D segmentation of Ewing’s sarcoma in MR images. Inf. Technol. Biomed. Adv. Intell. Soft Comput. 69(2), 91–101 (2010)CrossRefGoogle Scholar
  6. 6.
    Deneubourg, J., Pasteels, J., Verhaeghe, J.: Probabilistic behavior in ants—a strategy of errors. J. Theor. Biol. 105(2), 259–271 (1983)CrossRefGoogle Scholar
  7. 7.
    Doi, K.: Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput. Med. Imaging Graph. 31(4–5), 198–211 (2007)Google Scholar
  8. 8.
    Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B 26(1), 29–41 (1996)CrossRefGoogle Scholar
  9. 9.
    Gonzalez, R., Woods, R.: Digital Image Processing. Prentice Hall (2008)Google Scholar
  10. 10.
    Juszczyk, J., Pietka, E., Pycinski, B.: Granular computing in model based abdominal organs detection. Comput. Med. Imaging Graph. 46(2), 121–130 (2015)Google Scholar
  11. 11.
    Kawa, J., Juszczyk, J., Pycinski, B., Badura, P., Pietka, E.: Radiological atlas for patient specific model generation. Inf. Technol. Biomed. Adv. Intell. Syst. Comput. 284(4), 69–82 (2014)CrossRefGoogle Scholar
  12. 12.
    Kawa, J., Pietka, E.: Image clustering with median and myriad spatial constraint enhanced FCM. Comput. Recogn. Syst. Adv. Intell. Soft Comput. 45, 211–218 (2005)CrossRefGoogle Scholar
  13. 13.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings. IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)Google Scholar
  14. 14.
    Liang, Y., Zhang, M., Browne, W.: Image segmentation: a survey of methods based on evolutionary computation. In: Simulated Evolution and Learning, Lecture Notes in Computer Science, vol. 8886, pp. 847–859 (2014)Google Scholar
  15. 15.
    Maitra, M., Chatterjee, A.: A hybrid cooperative-comprehensive learning based PSO algorithm for image segmentation using multilevel thresholding. Expert Syst. Appl. 34(2), 1341–1350 (2008)CrossRefGoogle Scholar
  16. 16.
    Millonas, M.M.: Swarms, phase transitions, and collective intelligence. In: Artificial Life III. Addison-Wesley (1994)Google Scholar
  17. 17.
    Mohamad, M.S.: An enhancement of binary particle swarm optimization for gene selection in classifying cancer classes. Algorithm Mol. Biol. 8(1), 1–11 (2013)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Pham, D., Xu, C., Prince, J.: Current methods in medical image segmentation. Ann. Rev. Biomed. Eng. 2, 315–337 (2000)CrossRefGoogle Scholar
  19. 19.
    Pietka, E., Kawa, J., Spinczyk, D., Badura, P., Wieclawek, W., Czajkowska, J., Rudzki, M.: Role of radiologists in CAD life-cycle. Eur. J. Radiol. 78(2), 225–233 (2011)CrossRefGoogle Scholar
  20. 20.
    Roseffeld, S.: Critical junction: nonlinear dynamics, swarm intelligence and cancer research. In: IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, pp. 206–211 (2013)Google Scholar
  21. 21.
    Sharma, N., Ray, A., Sharma, S., Shukla, K., Pradhan, S., Aggarwal, L.: Segmentation and classification of medical images using texture-primitive features: application of BAM-type artificial neural network. J. Med. Phys. 33(3), 119–126 (2008)CrossRefGoogle Scholar
  22. 22.
    Simon, D.: Evolutionary Optimization Algorithms. John Wiley and Sons (2013)Google Scholar
  23. 23.
    Udupa, J., Samarasekera, S.: Fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation. Graph. Model Image Process. 58(3), 246–261 (1996)CrossRefGoogle Scholar
  24. 24.
    Verma, B., Zakos, J.: A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques. IEEE Trans. Inf. Technol. B 5(1), 46–54 (2001)CrossRefGoogle Scholar
  25. 25.
    Zarychta, P.: Features extraction in anterior and posterior cruciate ligaments analysis. Comput. Med. Imaging Graph. 46(2), 108–120 (2015)Google Scholar
  26. 26.
    Zyout, I., Czajkowska, J., Grzegorzek, M.: Multi-scale textural feature extraction and particle swarm optimization based model selection for false positive reduction in mammography. Comput. Med. Imaging Graph. 46(2), 95–107 (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Faculty of Biomedical EngineeringSilesian University of TechnologyZabrzePoland

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