Skip to main content

Advertisement

SpringerLink
Log in

Morphological algorithm design for binary images using genetic programming

  • Published:
Genetic Programming and Evolvable Machines Aims and scope Submit manuscript

Abstract

This paper presents a Genetic Programming (GP) approach to the design of Mathematical Morphology (MM) algorithms for binary images. The algorithms are constructed using logic operators and the basic MM operators, i.e. erosion and dilation, with a variety of structuring elements. GP is used to evolve MM algorithms that convert a binary image into another containing just a particular feature of interest. In the study we have tested three fitness functions, training sets with different numbers of elements, training images of different sizes, and 7 different features in two different kinds of applications. The results obtained show that it is possible to evolve good MM algorithms using GP.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. G. Adorni and S. Cagnoni, “Design of explicitly or implicitly parallel low-resolution character recognition algorithms by means of genetic programming,” in Soft Computing and Industry Recent Applications, R. Roy, M. Köppen, S. Ovaska, T. Furuhashi and F. Hoffmann, (eds.), Springer-Verlag, 10–24 2001. Published 2002, pp. 387–398.

  2. D. Andre, “Automatically defined features: The simultaneous evolution of 2-dimensional feature detectors and an algorithm for using them,” in Advances in Genetic Programming, Complex Adaptive Systems, K. E. Kinnear, (ed.), MIT Press, Cambridge, 1994, pp. 477–494.

  3. W. Banzhaf, P. Nordin, R. E. Keller and F. D. Francone, Genetic Programming — An Introduction; On the Automatic Evolution of Computer Programs and its Applications, Morgan Kaufmann, dpunkt.verlag, 1998.

  4. S. Cagnoni, A. B. Dobrzeniecki, R. Poli and J. C. Yanch, “Genetic algorithm-based interactive segmentation of 3D medical images,” Image and Vision Computing, vol. 17, pp. 881–895, 1999.

  5. C. Cinel, G. W. Humphreys and R. Poli, “Cross-modal illusory conjunctions between vision and touch,” Journal of Experimental Psychology: Human Perception and Performance, vol. 28, pp. 1243–1266, 2002.

  6. T. Crimmins and W. Brown, “Image algebra and automatic set recognition algorithms,” IEEE Transactions on Aerospace and Electronic Systems, vol. 21, pp. 60–69, 1985.

  7. J. M. Daida, T. F. Bersano-Begey, S. J. Ross and J. F. Vesecky, “Computer-assisted design of image classification algorithms: Dynamic and static fitness evaluations in a scaffolded genetic programming environment,” in Genetic Programming 1996: Proceedings of the First Annual Conference, J. R. Koza, D. E. Goldberg, D. B. Fogel and R. L. Riolo (eds.), MIT Press, Stanford University, CA, USA, 28–31, 1996, pp. 279–284.

  8. E. R. Dougherty and R. A. Latufo, Hands-on Morphological Image Processing, volume TT59. Spie Press, 2003.

  9. J. P. Egan, Signal Detection Theory and R.O.C. Analysis, Academic Press, 1975.

  10. R. C. Gonzalez and R. E. Woods, Digital Image Processing, Addison-Wesley, Reading, MA, USA, 3rd edn., 1992.

  11. C. T. M. Graae, P. Nordin and M. Nordahl, “Stereoscopic vision for a humanoid robot using genetic programming,” in Real-World Applications of Evolutionary Computing, S. Cagnoni, R. Poli, G. D. Smith, D. Corne, M. Oates, E. Hart, P. L. Lanzi, E. J. Willem, Y. Li, B. Paechter and T. C. Fogarty (eds.), volume 1803 of LNCS, Springer-Verlag, Edinburgh, 17, 2000, pp. 12–21.

  12. N. Harvey and S. Marshall, “The use of genetic algorithms in morphological filter design,” Signal Processing: Image Communication, vol. 8, pp. 55–72, 1996.

  13. N. R. Harvey, S. Perkins, S. P. Brumby, J. Theiler, R. B. Porter, A. C. Young, A. K. Varghese, J. J. Szymanski and J. J. Bloch, “Finding golf courses: The ultra high tech approach,” in Real World Applications of Evolutionary Computing, volume 1803 of Lecture Notes in Computer Science, S. C. et al., (eds.), Springer, 2000.

  14. N. R. Harvey, J. Theiler, S. P. Brumby, S. Perkins, J. J. Szymanski, J. J. Bloch, R. B. Porter, M. Galassi and A. C. Young, “Comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction,” IEEE Transactions on Geoscience and Remote Sensing, vol. 40, pp. 393–404, 2002.

  15. J. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, USA, 1975.

  16. D. Howard, S. C. Roberts and R. Brankin, “Target detection in imagery by genetic programming,” Advances in Engineering Software, vol. 30, pp. 303–311, 1999.

  17. J. R. Koza, Genetic programming: On the Programming of Computers by Natural Selection, MIT Press, Cambridge, Mass., 1992.

  18. W. B. Langdon and R. Poli, Foundations of Genetic Programming, Springer-Verlag, 2002.

  19. D. Marr, Vision, W.H. Freeman & Co., New York, 1982.

  20. Y. Nong, L. Yushu and X. Qifu, “The use of genetic algorithms in morphological filter design, ” in 5th International Conference on Signal Processing Proceedings WCCC-ICSP 2000, vol. 1, pp. 476–479, 2000.

  21. P. Nordin and W. Banzhaf, “Programmatic compression of images and sound, ” in Genetic Programming 1996: Proceedings of the First Annual Conference, J. R. Koza, D. E. Goldberg, D. B. Fogel and R. L. Riolo (eds.), Stanford University, MIT Press, USA, 28–31, 1996, pp. 345–350.

  22. R. Poli, “Genetic programming for image analysis,” in Genetic Programming 1996: Proceedings of the First Annual Conference, J. R. Koza, D. E. Goldberg, D. B. Fogel and R. L. Riolo, (eds.), MIT Press, Stanford University, CA, USA, pp. 363–368.

  23. R. Poli, “Parallel distributed genetic programming,” in New Ideas in Optimization, Advanced Topics in Computer Science, chapter 27, D. Corne, M. Dorigo and F. Glover (eds.), McGraw-Hill, Maidenhead, Berkshire, England, 1999, pp. 403–431.

  24. R. Poli and S. Cagnoni, “Genetic programming with user-driven selection: Experiments on the evolution of algorithms for image enhancement,” in Genetic Programming 1997: Proceedings of the Second Annual Conference, J. R. Koza, K. Deb, M. Dorigo, D. B. Fogel, M. Garzon, H. Iba and R. L. Riolo (eds.), Morgan Kaufmann, Stanford University, CA, USA, 1997, pp. 269–277.

  25. R. Poli and G. Valli, “Hopfield neural nets for the optimum segmentation of medical images ” in Handbook of Neural Computation, chapter G.5.5, E. Fiesler and R. Beale, (eds.), Oxford University Press, 1997.

  26. M. I. Quintana, Genetic programming applied to morphological image processing, PhD thesis, School of Computer Science, University of Birmingham, Birmingham, UK, 2005.

  27. M. I. Quintana, R. Poli and E. Claridge, “Genetic programming for mathematical morphology algorithm design on binary images,” in Artificial Intelligence, Proceedings of the International Conference KBCS-2002, Vikas, M. Sasikumar, J. Hedge and M. Khavita (eds.), 2002, pp. 161–171.

  28. M. I. Quintana, R. Poli and E. Claridge, “On two approaches to image processing algorithm design for binary images using GP,” in Applications of Evolutionary Computing, EvoWorkshops2003: EvoBIO, EvoCOP, EvoIASP, EvoMUSART, EvoROB, EvoSTIM, G. R. Raidl, S. Cagnoni, J. J. R. Cardalda, D. W. Corne, J. Gottlieb, A. Guillot, E. Hart, C. G. Johnson, E. Marchiori, J.-A. Meyer and M. Middendorf (eds.), volume 2611 of LNCS, University of Essex, Springer-Verlag, England, UK, 14-16 2003, pp. 426–435.

  29. M. E. Roberts and E. Claridge, “An artificially evolved vision system for segmenting skin lesion images,” in Proceedings of the 6th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), R. E. Ellis and T. M. Peters (eds.), volume 2878 of LNCS, Springer-Verlag, Montreal, Canada, 2003, pp. 655–662.

  30. M. E. Roberts and E. Claridge, “A multistage approach to cooperatively coevolving feature construction and object detection,” in Applications of Evolutionary Computing, EvoWorkshops2005: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, EvoSTOC, volume 3449 of LNCS, F. Rothlauf, J. Branke, S. Cagnoni, D. W. Corne, R. Drechsler, Y. Jin, P. Machado, E. Marchiori, J. Romero, G. D. Smith and G. Squillero (eds.), Springer Verlag, Lausanne, Switzerland, 30–1, 2005, pp. 396–406.

  31. P. Rosin and J. Hervas, “Image thresholding for landslide detection by genetic programming,” in Analysis of multi-temporal remote sensing images, L. Bruzzone and P. Smiths (eds.), World Scientific, 2002, pp. 65–72.

  32. C. Ryan, Automatic Re-engineering of Software Using Genetic Programming, volume 2 of Genetic Programming, Kluwer Academic Publishers, 1, 1999.

  33. M. Schmitt, “Mathematical morphology and artificial intelligence: An automatic programming system,“ Signal Processing, vol. 16, pp. 389–401, 1989.

  34. J. Serra, Image Analysis and Mathematical Morphology, Academic Press, 1982.

  35. J. Serra, Image Analysis and Mathematical Morphology, vol. 2, Academic Press, 1989.

  36. L. Spector, Automatic Quantum Computer Programming: A Genetic Programming Approach, volume 7 of Genetic Programming, Kluwer Academic Publishers, Boston/Dordrecht/New York/London, 2004.

  37. S. A. Stanhope and J. M. Daida, “Genetic programming for automatic target classification and recognition in synthetic aperture radar imagery,“ in Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming, volume 1447 of LNCS, V. W. Porto, N. Saravanan, D. Waagen and A. E. Eiben (eds.), Mission Valley Marriott, Springer-Verlag, San Diego, California, USA, 25–27, 1998, pp. 735–744.

  38. W. A. Tackett, “Genetic programming for feature discovery and image discrimination,“ in Proceedings of the 5th International Conference on Genetic Algorithms, ICGA-93, S. Forrest (ed.), University of Illinois at Urbana-Champaign, Morgan Kaufmann, 17–21, 1993, pp. 303–309.

  39. H. Takagi, “Interactive evolutionary computation: Fusion of the capabilities of EC optimization and human evaluation,” in Proc. of the IEEE, 2001, vol. 89, pp. 1275–1296, Invited Paper.

  40. A. Teller and M. Veloso, “Algorithm evolution for face recognition: What makes a picture difficult,” in International Conference on Evolutionary Computation, IEEE Press, Perth, Australia, 1995, pp. 608–613.

  41. A. Teller and M. Veloso, PADO: Learning tree structured algorithms for orchestration into an object recognition system, Technical Report CMU-CS-95-101, Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA, 1995.

  42. R. C. Vogt, Automatic Generation of Morphological Set Recognition Algorithms, Springer-Verlag, New York, 1989.

  43. J. F. Winkeler and B. S. Manjunath, “Genetic programming for object detection,” in Genetic Programming 1997: Proceedings of the Second Annual Conference, J. R. Koza, K. Deb, M. Dorigo, D. B. Fogel, M. Garzon, H. Iba and R. L. Riolo (eds.), Morgan Kaufmann, Stanford University, CA, USA, 13–16, 1997, pp. 330–335.

  44. I. Yoda, K. Yamamoto and H. Yamada, “Automatic acquisition of hierarchical mathematical morphology procedures by genetic algorithms,” Image and Vision Computing, vol. 17, pp. 749–760, 1999.

  45. M. Yu, N. Eua-anant, A. Saudagar and L. Udpa, “Genetic algorithm approach to image segmentation using morphological operations,” in International Conference on Image Processing, vol. 3, pp. 775–779, 1998.

  46. M. Zhang, V. B. Ciesielski and P. Andreae, “A domain-independent window approach to multiclass object detection using genetic programming.” EURASIP Journal on Applied Signal Processing, 2003(8):841–859, July 2003. Special Issue on Genetic and Evolutionary Computation for Signal Processing and Image Analysis.

  47. M. Zhang and W. Smart, “Learning weights in genetic programs using gradient descent for object recognition,” in Applications of Evolutionary Computing, EvoWorkshops2005: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, EvoSTOC, F. Rothlauf, J. Branke, S. Cagnoni, D. W. Corne, R. Drechsler, Y. Jin, P. Machado, E. Marchiori, J. Romero, G. D. Smith and G. Squillero (eds.), volume 3449 of LNCS, Springer Verlag., Lausanne, Switzerland, 30–1, 2005, pp. 408–417.

  48. D. Zongker, B. Punch and B. Rand, Lilgp 1.01. User Manual, Technical report, Michigan State University, 1996.

Download references

Author information

Authors and Affiliations

  1. CIEP, Facultad de Ingeniería, Universidad Autónoma de San Luis Potosí, Av. Dr. Manuel Nava 8, C.P. 78290, San Luis Potosí S.L.P., México

    Marcos I. Quintana

  2. Department Computer Science, University of Essex, Colchester, CO4 3SQ, UK

    Riccardo Poli

  3. School of Computer Science, The University of Birmingham, Birmingham, B15 2TT, UK

    Ela Claridge

Authors
  1. Marcos I. Quintana
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Riccardo Poli
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ela Claridge
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marcos I. Quintana.

Additional information

Communicated by: Lee Spector

Rights and permissions

Reprints and permissions

About this article

Cite this article

Quintana, M.I., Poli, R. & Claridge, E. Morphological algorithm design for binary images using genetic programming. Genet Program Evolvable Mach 7, 81–102 (2006). https://doi.org/10.1007/s10710-006-7012-3

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10710-006-7012-3

Keywords

Search

Navigation