Pattern Analysis and Applications

, Volume 14, Issue 4, pp 425–440 | Cite as

Adaptive potential active contours

  • Arkadiusz TomczykEmail author
  • Piotr S. Szczepaniak
Theoretical Advances


In this paper potential active contours are presented as a new method of image segmentation. The concept of potential contour is a result of the relationship between active contour techniques and the methods of classifiers’ construction. The proposed method can be extended by the adaptation mechanism that allows changing the available class of the shapes dynamically. An original contribution is also the method of evaluation of segmentation results and methodology used for the parameters selection. The described method is illustrated by two examples.


Active contour Image segmentation Classification 



Authors would like to express their gratitude to Mr Cyprian Wolski, MD, from the Department of Radiology and Diagnostic Imaging of Barlicki University Hospital in Lodz for making heart images available and sharing his medical knowledge. This work has been partly supported by the Ministry of Science and Higher Education, Republic of Poland, under project number N 519 007 32/0978; decision no. 0978/T02/2007/32.


  1. 1.
    Amini AA, Weymouth TE, Jain RC (1990) Using dynamic programming for solving variatioal problems in vision. IEEE Trans Pattern Anal Mach Intell 12(9):855–867CrossRefGoogle Scholar
  2. 2.
    Bishop C (1993) Neural networks for pattern recognition. Clarendon Press, OxfordGoogle Scholar
  3. 3.
    Caselles V (1995) Geometric models for active contours. In: Proceedings of the international conference on image processing, pp 9–12Google Scholar
  4. 4.
    Caselles V, Kimmel R, Sapiro G (1997) Geodesic active contours. Int J Comput Vis 22(1):61–79zbMATHCrossRefGoogle Scholar
  5. 5.
    Casseles V, Catte F, Coll T, Dibos F (1993) A geometric model for active contours in image processing. Numer Math 66:1–31MathSciNetCrossRefGoogle Scholar
  6. 6.
    Cichosz P (2000) Learning systems. WNT, Warsaw (in Polish)Google Scholar
  7. 7.
    Cohen LD (1991) On active contour models and balloons. Comput Vis Graphics Image Process Image Underst 53(2):211–218zbMATHGoogle Scholar
  8. 8.
    Cohen LD, Cohen I (1991) Finite element methods for active contour models and balloons for 2d and 3d images. IEEE Trans Pattern Anal Mach Intell 15(11):1131–1147CrossRefGoogle Scholar
  9. 9.
    Cootes T, Taylor CJ (1992) Active shape models—smart snakes. In: Proceedings of 3rd British machine vision conference, Springer, pp 266–275Google Scholar
  10. 10.
    Cootes T, Taylor C, Cooper D, Graham J (1994) Active shape model—their training and application. CVGIP Image Underst 61(1):38–59Google Scholar
  11. 11.
    Cootes TF, Hill A, Taylor CJ, Haslam J (1993) The use of active shape models for locating structures in medical images. In: Proceedings of the 13th international conference on information processing in medical imaging, Springer, pp 33–47Google Scholar
  12. 12.
    Davies ER (2005) Machine vision, theory, algorithms, practicalities. Elsevier/Morgan Kaufmann, San FranciscoGoogle Scholar
  13. 13.
    Delingette H, Montagnat J (2000) New algorithms for controlling active contours shape and topology. In: European conference on computer vision, pp 381–395Google Scholar
  14. 14.
    Denzler J, Niemann H (1996) Active rays: a new approach to contour tracking. Int J Comput Inf Technol 4:9–16Google Scholar
  15. 15.
    Gonzalez R, Woods R (2002) Digital image processing. Prentice-Hall, New JerseyGoogle Scholar
  16. 16.
    Grzeszczuk R, Levin D (1997) Brownian strings: segmenting images with stochastically deformable models. IEEE Trans Pattern Anal Mach Intell 19(10):1100–1113CrossRefGoogle Scholar
  17. 17.
    Ivins J, Porrill J (1994) Active region models for segmenting medical images. In: IEEE international conference on image processing, pp 227–231Google Scholar
  18. 18.
    Jacob M, Blu T, Unser M (2001) A unifying approach and interface for spline-based snakes. In: Proc SPIE Med Imaging, l. 4322:340–347Google Scholar
  19. 19.
    Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Int J Comput Vis 1(4):321–331Google Scholar
  20. 20.
    Kichenassamy S, Kumar A, Olver PJ, Tannenbaum A, Yezzi AJ (1995) Gradient flows and geometric active contour models. In: ICCV, pp 810–815Google Scholar
  21. 21.
    Kirkpatrick S (1984) Optimization by simulated annealing—quantitative studies. J Stat Phys 34:975–986MathSciNetCrossRefGoogle Scholar
  22. 22.
    Kirkpatrick S, Gelatt CDJ, Vecchi MP (1983) Optimization by simulated annealing. Sci Agric 220(4598):671–680MathSciNetzbMATHGoogle Scholar
  23. 23.
    Koronacki J, Cwik J (2005) Statistical learning systems. WNT, Warsaw (in Polish)Google Scholar
  24. 24.
    Kwiatkowski W (2001) Methods of automatic pattern recognition. Wojskowa Akademia Techniczna, Warsaw (in Polish)Google Scholar
  25. 25.
    Leroy B, Herlin IL, Cohen LD (1996) Multi-resolution algorithms for active contour models. In: 12th international conference on analysis and optimization of systems, images, wavelets and PDEs, Lecture Notes in Control and Information Sciences, Springer, pp 58–65Google Scholar
  26. 26.
    Looney C (1999) Pattern recognition using neural networks, theory and algorithms for engineers and scientists. Oxford University Press, New YorkGoogle Scholar
  27. 27.
    Malladi R, Sethian JA, Vemuri BC (1995) Shape modeling with front propagation: a level set approach. IEEE Trans Pattern Anal Mach Intell 17(2):158–175CrossRefGoogle Scholar
  28. 28.
    McInerney T, Terzopoulos D (1995) Topologically adaptable snakes. In: ICCV, pp 840–845Google Scholar
  29. 29.
    Metropolis N, W RA, Rosenbluth MN, Teller AH, Teller E (1953) Equations of state calculations by fast computing machines. J Chem Phys 21:1087–1092CrossRefGoogle Scholar
  30. 30.
    Osher S, Sethian JA (1988) Fronts propagating with curvature dependent speed: algorithms based on Hamilton–Jacobi formulations. J Comput Phys 79:12–49MathSciNetzbMATHCrossRefGoogle Scholar
  31. 31.
    Ossowski S (2001) Neural networks for information processing. Oficyna Wydawnicza Politechniki Warszawskiej, Warsaw (in Polish)Google Scholar
  32. 32.
    Pedrycz W (2005) Knowlege-based clustering. Wiley-Interscience, HobokenCrossRefGoogle Scholar
  33. 33.
    Pincus M (1970) A Monte Carlo method for the approximate solution of certain types of constrained optimization problems. Oper Res 18:1225–1228MathSciNetzbMATHCrossRefGoogle Scholar
  34. 34.
    Rutkowski L (2005) Methods and techniques of artificial intelligence. Wydawnictwo Naukowe PWN, Warsaw (in Polish)Google Scholar
  35. 35.
    Schnabel J, Arridge S (1995) Active contour models for shape description using multiscale differential invariants. In: Pycock D (ed) Proceedings of British machine vision conference, pp 197–206Google Scholar
  36. 36.
    Sonka M, Hlavec V, Boyle R (1994) Image processing, analysis and machine vision. Chapman and Hall, CambridgeGoogle Scholar
  37. 37.
    Staib LH, Duncan JS (1989) Parametrically deformable contour models. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition, pp 98–103Google Scholar
  38. 38.
    Stapor K (2005) Automatic classification of the objects. Akademicka Oficyna Wydawnicza EXIT, Warsaw (in Polish)Google Scholar
  39. 39.
    Szczepaniak PS (2004) Intelligent computing, fast transforms and classifiers. Akademicka Oficyna Wydawnicza EXIT, Warsaw (in Polish)Google Scholar
  40. 40.
    Tadeusiewicz R (1993) Neural networks. Akademicka Oficyna Wydawnicza, Warsaw (in Polish)Google Scholar
  41. 41.
    Tadeusiewicz R, Flasinski M (1991) Pattern recognition. Wydawnictwo Naukowe PWN, Warsaw (in Polish)Google Scholar
  42. 42.
    Tomczyk A (2007) Image segmentation using adaptive potential active contour. In: Kurzynski M, Puchala E, Wozniak M, Zolnierek A (eds) Computer recognition systems 2, advances in intelligent and soft computing, Springer, pp 148–155Google Scholar
  43. 43.
    Tomczyk A, Szczepaniak PS (2006) Adaptive potential active hypercontours. In: Rutkowski L, Tadeusiewicz R, Zadeh LA, Zurada J (eds) Artificial intelligence and soft computing—ICAISC 2006, 8th international conference, Zakopane, Poland, June 25–29, Proceedings, Lecture Notes in Computer Science, Springer, pp 692–701Google Scholar
  44. 44.
    Tomczyk A, Szczepaniak PS (2007) Contribution of active contour approach to image understanding. In: Proceedings of IEEE international workshop on imaging systems and techniques, IEEEGoogle Scholar
  45. 45.
    Tomczyk A, Szczepaniak PS (2005) On the relationship between active contours and contextual classification. In: Kurzynski M, Wozniak M, Puchala E, Zolnierek A (eds) Computer recognition systems. In: Proceedings of the 4th international conference on computer recognition systems, CORES’05, May 22–25, 2005, Rydzyna Castle, Poland, Advances in Soft Computing, Springer, pp 303–311Google Scholar
  46. 46.
    Tomczyk A, Szczepaniak PS, Pryczek M (2007) Active contours as knowledge discovery methods. In: Corrouble V, Takeda M, Suzuki E (eds) Discovery science, 10th international conference, DS 2007, Sendai, Japan, October 1–4, 2007, Proceedings, Lecture Notes in Computer Science, Springer, pp 209–218Google Scholar
  47. 47.
    Tomczyk A, Wolski C, Szczepaniak PS, Rotkiewicz A (2009) Analysis of changes in heart ventricle shape using contextual potential active contours. In: Kurzynski M, Wozniak M (eds) Computer recognition systems 3, advances in intelligent and soft computing, Springer, pp 397–405Google Scholar
  48. 48.
    Williams DJ, Shah M (1990) A fast algorithm for active contours. In: Proceedings of 3rd international conference on computer vision, pp 592–595Google Scholar
  49. 49.
    Xu C, Prince J (1998) Snakes, shapes, and gradient vector flow. In: IEEE transactions on image processing 7(2), IEEE, pp 359–369Google Scholar
  50. 50.
    Xu C, Yezzi A, Prince J (2000) On the relationship between parametric and geometric active contours. In: 34th Asilomar conference on signals, systems and computers, pp 483–489Google Scholar
  51. 51.
    Xu C, Hopkins J, Yezzi A, Prince JL (2001) A summary of geometric level-set analogues for a general class of parametric active contour and surface models. In: Proc of 1st IEEE workshop on variational and level set methods in computer vision, pp 104–111Google Scholar
  52. 52.
    Yezzi A, Kichenassamy S, Kumar A, Olver P, Tannenbaum A (1997) A geometric snake model for segmentation of medical imagery. IEEE Trans Med Imaging 16(2)Google Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Institute of Information Technology, Technical University of LodzLodzPoland

Personalised recommendations