Advertisement

Automatic Fitting of Feature Points for Border Detection of Skin Lesions in Medical Images with Bat Algorithm

  • Akemi Gálvez
  • Iztok Fister
  • Iztok FisterJr.
  • Eneko Osaba
  • Javier Del Ser
  • Andrés Iglesias
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 798)

Abstract

This paper addresses the problem of automatic fitting of feature points for border detection of skin lesions. This problem is an important task in segmentation of dermoscopy images for semi-automatic early diagnosis of melanoma and other skin lesions. Given a set of feature points selected by a dermatologist, we apply a powerful nature-inspired metaheuristic optimization method called bat algorithm to obtain the free-form parametric Bézier curve that fits the points better in the least-squares sense. Our experimental results on two examples of skin lesions show that the method performs quite well and might be applied to automatic fitting of feature points for border detection in medical images.

Keywords

Computational intelligence Medical images Skin lesion Border detection Nature-inspired metaheuristic techniques Bat algorithm 

Notes

Acknowledgments

This research work has been kindly supported by the project PDE-GIR of the European Union’s Horizon 2020 research and innovation programme, Marie Sklodowska-Curie grant agreement No 778035, the Spanish Ministry of Economy and Competitiveness (Computer Science National Program), grant #TIN2017-89275-R of the Agencia Estatal de Investigación and European Regional Development Funds (AEI/FEDER-UE), the project #JU12 of SODERCAN and European Regional Development Funds (SODERCAN/FEDER-UE), the Slovenian Research Agency (Research Core Funding No. P2-0057), and the project EMAITEK of the Basque Government.

References

  1. 1.
    Abbas, A.A., Guo, X., Tan, W.H., Jalab, H.A.: Combined spline and B-spline for an improved automatic skin lesion segmentation in dermoscopic images using optimal color channel. J. Med. Syst. 38, 80–80 (2014)CrossRefGoogle Scholar
  2. 2.
    Alihodzic, A., Tuba, M.: Improved bat algorithm applied to multilevel image thresholding. Sci. World J. 2014, 16 pages (2014). article ID 176718Google Scholar
  3. 3.
    Argenziano, G., Soyer, H.P., De Giorgi, V.: Dermoscopy: A Tutorial. EDRA Medical Publishing & New Media, Milan (2002)Google Scholar
  4. 4.
    Barhak, J., Fischer, A.: Parameterization and reconstruction from 3D scattered points based on neural network and PDE techniques. IEEE Trans. Vis. Comput. Graph. 7(1), 1–16 (2001)CrossRefGoogle Scholar
  5. 5.
    Celebi, M.E., H. Iyatomi, H., Schaefer, G., Stoecker, W.V.: Lesion border detection in dermoscopy images. Comput. Med. Imaging Graph. 33(2), 148–153 (2009)CrossRefGoogle Scholar
  6. 6.
    Dierckx, P.: Curve and Surface Fitting with Splines. Oxford University Press, Oxford (1993)zbMATHGoogle Scholar
  7. 7.
    Engelbrecht, A.P.: Fundamentals of Computational Swarm Intelligence. Wiley, Chichester (2005)Google Scholar
  8. 8.
    Fister, I., Rauter, S., Yang, X.-S., Ljubic, K., Fister Jr., I.: Planning the sports training sessions with the bat algorithm. Neurocomputing 149, Part B, 993–1002 (2015)CrossRefGoogle Scholar
  9. 9.
    Friedman, R.J., Rigel, D.S., Kopf, A.W.: Early detection of malignant melanoma: the role of physician examination and self-examination of the skin. Cancer J. Clin. 35(3), 130–151 (1985)CrossRefGoogle Scholar
  10. 10.
    Gálvez, A., Iglesias, A.: Efficient particle swarm optimization approach for data fitting with free knot B-splines. Comput. Aided Des. 43(12), 1683–1692 (2011)CrossRefGoogle Scholar
  11. 11.
    Gálvez, A., Iglesias, A.: Firefly algorithm for explicit B-Spline curve fitting to data points. Math. Probl. Eng., Article ID 528215, 12 pages (2013)Google Scholar
  12. 12.
    Gálvez A., Iglesias A.: From nonlinear optimization to convex optimization through firefly algorithm and indirect approach with applications to CAD/CAM. Sci. World J. Article ID 283919, 10 pages (2013)Google Scholar
  13. 13.
    Gálvez, A., Iglesias, A.: New memetic self-adaptive firefly algorithm for continuous optimization. Int. J. Bio Inspired Comput. 8(5), 300–317 (2016)CrossRefGoogle Scholar
  14. 14.
    Gálvez, A., Iglesias, A., Avila, A., Otero, C., Arias, R., Manchado, C.: Elitist clonal selection algorithm for optimal choice of free knots in B-spline data fitting. Appl. Soft Comput. 26, 90–106 (2015)CrossRefGoogle Scholar
  15. 15.
    Gálvez, A., Iglesias, A., Cobo, A., Puig-Pey, J., Espinola, J.: Bézier curve and surface fitting of 3D point clouds through genetic algorithms, functional networks and least-squares approximation. Lectures Notes in Computer Science, vol. 4706, pp. 680–693 (2007)Google Scholar
  16. 16.
    Garnavi, R., Aldeen, M., Celebi, M.E., Varigos, G., Finch, S.: Border detection in dermoscopy images using hybrid thresholding on optimized color channels. Comput. Med. Imaging Graph. 35(2), 105–115 (2011)CrossRefGoogle Scholar
  17. 17.
    Gu, P., Yan, X.: Neural network approach to the reconstruction of free-form surfaces for reverse engineering. Comput. Aided Des. 27(1), 59–64 (1995)CrossRefGoogle Scholar
  18. 18.
    Hoffmann, M.: Numerical control of Kohonen neural network for scattered data approximation. Numer. Algorithms 39, 175–186 (2005)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Iglesias, A., Echevarría, G., Gálvez, A.: Functional networks for B-spline surface reconstruction. Futur. Gener. Comput. Syst. 20(8), 1337–1353 (2004)CrossRefGoogle Scholar
  20. 20.
    Iglesias, A., Gálvez, A., Collantes, M.: Multilayer embedded bat algorithm for B-spline curve reconstruction. Integr. Comput. Aided Eng. 24(4), 385–399 (2017)CrossRefGoogle Scholar
  21. 21.
    Jing, L., Sun, L.: Fitting B-spline curves by least squares support vector machines. In: Proceedings of the 2nd International Conference on Neural Networks & Brain, Beijing (China), pp. 905–909. IEEE Press (2005)Google Scholar
  22. 22.
    Jupp, D.L.B.: Approximation to data by splines with free knots. SIAM J. Numer. Anal. 15, 328–343 (1978)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Kashi, S., Minuchehr, A., Poursalehi, N., Zolfaghari, A.: Bat algorithm for the fuel arrangement optimization of reactor core. Ann. Nucl. Energy 64, 144–151 (2014)CrossRefGoogle Scholar
  24. 24.
    Kaveh, A., Zakian, P.: Enhanced bat algorithm for optimal design of skeletal structures. Asian J. Civ. Eng. 15(2), 179–212 (2014)Google Scholar
  25. 25.
    Kennedy, J., Eberhart, R.C., Shi, Y.: Swarm Intelligence. Morgan Kaufmann Publishers, San Francisco (2001)Google Scholar
  26. 26.
    Knopf, G.K., Kofman, J.: Adaptive reconstruction of free-form surfaces using Bernstein basis function networks. Eng. Appl. Artif. Intell. 14(5), 577–588 (2001)CrossRefGoogle Scholar
  27. 27.
    Latif, A., Palensky, P.: Economic dispatch using modified bat algorithm. Algorithms 7(3), 328–338 (2014)CrossRefGoogle Scholar
  28. 28.
    Ma, Z., Tavares, J.M.: A novel approach to segment skin lesions in dermoscopic images based on a deformable model. IEEE J. Biomed. Health Inform. 20, 615–623 (2016)CrossRefGoogle Scholar
  29. 29.
    Machado, D.A., Giraldi, G., Novotny, A.A.: Multi-object segmentation approach based on topological derivative and level set method. Integr. Comput. Aided Eng. 18, 301–311 (2011)CrossRefGoogle Scholar
  30. 30.
    Nachbar, F., Stolz, W., Merkle, T., Cognetta, A.B., Vogt, T., Landthaler, M., Bilek, P., Braun-Falco, O., Plewig, G.: The ABCD rule of dermatoscopy. High prospective value in the diagnosis of doubtful melanocytic skin lesions. J. Am. Acad. Dermatol. 30(4), 551–559 (1994)CrossRefGoogle Scholar
  31. 31.
    Park, H.: An error-bounded approximate method for representing planar curves in B-splines. Comput. Aided Geom. Des. 21, 479–497 (2004)MathSciNetCrossRefGoogle Scholar
  32. 32.
    Park, H., Lee, J.H.: B-spline curve fitting based on adaptive curve refinement using dominant points. Comput. Aided Des. 39, 439–451 (2007)CrossRefGoogle Scholar
  33. 33.
    Schmid, P.: Segmentation of digitized dermatoscopic images by two-dimensional color clustering. IEEE Trans. Med. Imaging 18(2), 164–171 (1999)CrossRefGoogle Scholar
  34. 34.
    Suárez, P., Iglesias, A.: Bat algorithm for coordinated exploration in swarm robotics. Adv. Intell. Syst. Comput. 514, 134–144 (2017)CrossRefGoogle Scholar
  35. 35.
    Suárez, P., Gálvez, A., Iglesias, A.: Autonomous coordinated navigation of virtual swarm bots in dynamic indoor environments by bat algorithm. In: International Conference in Swarm Intelligence, ICSI 2017. Lecture Notes in Computer Science, vol. 10386, pp. 176–184 (2017)Google Scholar
  36. 36.
    Suárez, P., Iglesias, A., Gálvez, A.: Make robots be bats: specializing robotic swarms to the bat algorithm. Swarm Evol. Comput. (2018, in press). https://www.sciencedirect.com/science/article/abs/pii/S2210650217306338
  37. 37.
    Ulker, E., Arslan, A.: Automatic knot adjustment using an artificial immune system for B-spline curve approximation. Inf. Sci. 179, 1483–1494 (2009)CrossRefGoogle Scholar
  38. 38.
    Wang, W.P., Pottmann, H., Liu, Y.: Fitting B-spline curves to point clouds by curvature-based squared distance minimization. ACM Trans. Graph. 25(2), 214–238 (2006)CrossRefGoogle Scholar
  39. 39.
    World Cancer Report 2014. World Health Organization. Chapter 5.14 (2014)Google Scholar
  40. 40.
    Yang, X.-S.: Nature-Inspired Metaheuristic Algorithms, 2nd edn. Luniver Press, Frome (2010)Google Scholar
  41. 41.
    Yang, X.S.: A new metaheuristic bat-inspired algorithm. Stud. Comput. Intell. 284, 65–74 (2010)zbMATHGoogle Scholar
  42. 42.
    Yang, X.S.: Bat algorithm for multiobjective optimization. Int. J. Bio Inspired Comput. 3(5), 267–274 (2011)CrossRefGoogle Scholar
  43. 43.
    Yang, X.S., Gandomi, A.H.: Bat algorithm: a novel approach for global engineering optimization. Eng. Comput. 29(5), 464–483 (2012)CrossRefGoogle Scholar
  44. 44.
    Yang, X.S.: Bat algorithm: literature review and applications. Int. J. Bio Inspired Comput. 5(3), 141–149 (2013)CrossRefGoogle Scholar
  45. 45.
    Yoshimoto, F., Moriyama, M., Harada, T.: Automatic knot adjustment by a genetic algorithm for data fitting with a spline. In: Proceedings of Shape Modeling International 1999, pp. 162–169. IEEE Computer Society Press (1999)Google Scholar
  46. 46.
    Yoshimoto, F., Harada, T., Yoshimoto, Y.: Data fitting with a spline using a real-coded algorithm. Comput. Aided Des. 35, 751–760 (2003)CrossRefGoogle Scholar
  47. 47.
    Zhou, H., Schaefer, G., Sadka, A., Celebi, M.E.: Anisotropic mean shift based fuzzy c-means segmentation of dermoscopy images. IEEE J. Sel. Top. Signal Process. 3(1), 26–34 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Akemi Gálvez
    • 1
    • 2
  • Iztok Fister
    • 3
  • Iztok FisterJr.
    • 3
  • Eneko Osaba
    • 4
  • Javier Del Ser
    • 4
    • 5
    • 6
  • Andrés Iglesias
    • 1
    • 2
  1. 1.Toho UniversityFunabashiJapan
  2. 2.Universidad de CantabriaSantanderSpain
  3. 3.University of MariborMariborSlovenia
  4. 4.TECNALIADerioSpain
  5. 5.University of the Basque Country (UPV/EHU)BilbaoSpain
  6. 6.Basque Center for Applied Mathematics (BCAM)BilbaoSpain

Personalised recommendations