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Adaptive potential active contours

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Abstract

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.

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References

  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):855–867

    Article  Google Scholar 

  2. Bishop C (1993) Neural networks for pattern recognition. Clarendon Press, Oxford

    Google Scholar 

  3. Caselles V (1995) Geometric models for active contours. In: Proceedings of the international conference on image processing, pp 9–12

  4. Caselles V, Kimmel R, Sapiro G (1997) Geodesic active contours. Int J Comput Vis 22(1):61–79

    Article  MATH  Google Scholar 

  5. Casseles V, Catte F, Coll T, Dibos F (1993) A geometric model for active contours in image processing. Numer Math 66:1–31

    Article  MathSciNet  Google Scholar 

  6. Cichosz P (2000) Learning systems. WNT, Warsaw (in Polish)

  7. Cohen LD (1991) On active contour models and balloons. Comput Vis Graphics Image Process Image Underst 53(2):211–218

    MATH  Google Scholar 

  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):1131–1147

    Article  Google Scholar 

  9. Cootes T, Taylor CJ (1992) Active shape models—smart snakes. In: Proceedings of 3rd British machine vision conference, Springer, pp 266–275

  10. Cootes T, Taylor C, Cooper D, Graham J (1994) Active shape model—their training and application. CVGIP Image Underst 61(1):38–59

    Google Scholar 

  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 33–47

  12. Davies ER (2005) Machine vision, theory, algorithms, practicalities. Elsevier/Morgan Kaufmann, San Francisco

    Google Scholar 

  13. Delingette H, Montagnat J (2000) New algorithms for controlling active contours shape and topology. In: European conference on computer vision, pp 381–395

  14. Denzler J, Niemann H (1996) Active rays: a new approach to contour tracking. Int J Comput Inf Technol 4:9–16

    Google Scholar 

  15. Gonzalez R, Woods R (2002) Digital image processing. Prentice-Hall, New Jersey

    Google Scholar 

  16. Grzeszczuk R, Levin D (1997) Brownian strings: segmenting images with stochastically deformable models. IEEE Trans Pattern Anal Mach Intell 19(10):1100–1113

    Article  Google Scholar 

  17. Ivins J, Porrill J (1994) Active region models for segmenting medical images. In: IEEE international conference on image processing, pp 227–231

  18. Jacob M, Blu T, Unser M (2001) A unifying approach and interface for spline-based snakes. In: Proc SPIE Med Imaging, l. 4322:340–347

  19. Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Int J Comput Vis 1(4):321–331

    Google Scholar 

  20. Kichenassamy S, Kumar A, Olver PJ, Tannenbaum A, Yezzi AJ (1995) Gradient flows and geometric active contour models. In: ICCV, pp 810–815

  21. Kirkpatrick S (1984) Optimization by simulated annealing—quantitative studies. J Stat Phys 34:975–986

    Article  MathSciNet  Google Scholar 

  22. Kirkpatrick S, Gelatt CDJ, Vecchi MP (1983) Optimization by simulated annealing. Sci Agric 220(4598):671–680

    MathSciNet  MATH  Google Scholar 

  23. Koronacki J, Cwik J (2005) Statistical learning systems. WNT, Warsaw (in Polish)

  24. Kwiatkowski W (2001) Methods of automatic pattern recognition. Wojskowa Akademia Techniczna, Warsaw (in Polish)

  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 58–65

  26. Looney C (1999) Pattern recognition using neural networks, theory and algorithms for engineers and scientists. Oxford University Press, New York

    Google Scholar 

  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):158–175

    Article  Google Scholar 

  28. McInerney T, Terzopoulos D (1995) Topologically adaptable snakes. In: ICCV, pp 840–845

  29. Metropolis N, W RA, Rosenbluth MN, Teller AH, Teller E (1953) Equations of state calculations by fast computing machines. J Chem Phys 21:1087–1092

    Article  Google Scholar 

  30. Osher S, Sethian JA (1988) Fronts propagating with curvature dependent speed: algorithms based on Hamilton–Jacobi formulations. J Comput Phys 79:12–49

    Article  MathSciNet  MATH  Google Scholar 

  31. Ossowski S (2001) Neural networks for information processing. Oficyna Wydawnicza Politechniki Warszawskiej, Warsaw (in Polish)

  32. Pedrycz W (2005) Knowlege-based clustering. Wiley-Interscience, Hoboken

    Book  Google Scholar 

  33. Pincus M (1970) A Monte Carlo method for the approximate solution of certain types of constrained optimization problems. Oper Res 18:1225–1228

    Article  MathSciNet  MATH  Google Scholar 

  34. Rutkowski L (2005) Methods and techniques of artificial intelligence. Wydawnictwo Naukowe PWN, Warsaw (in Polish)

  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 197–206

  36. Sonka M, Hlavec V, Boyle R (1994) Image processing, analysis and machine vision. Chapman and Hall, Cambridge

    Google Scholar 

  37. Staib LH, Duncan JS (1989) Parametrically deformable contour models. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition, pp 98–103

  38. Stapor K (2005) Automatic classification of the objects. Akademicka Oficyna Wydawnicza EXIT, Warsaw (in Polish)

  39. Szczepaniak PS (2004) Intelligent computing, fast transforms and classifiers. Akademicka Oficyna Wydawnicza EXIT, Warsaw (in Polish)

  40. Tadeusiewicz R (1993) Neural networks. Akademicka Oficyna Wydawnicza, Warsaw (in Polish)

  41. Tadeusiewicz R, Flasinski M (1991) Pattern recognition. Wydawnictwo Naukowe PWN, Warsaw (in Polish)

  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 148–155

  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 692–701

  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, IEEE

  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 303–311

  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 209–218

  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 397–405

  48. Williams DJ, Shah M (1990) A fast algorithm for active contours. In: Proceedings of 3rd international conference on computer vision, pp 592–595

  49. Xu C, Prince J (1998) Snakes, shapes, and gradient vector flow. In: IEEE transactions on image processing 7(2), IEEE, pp 359–369

  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 483–489

  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 104–111

  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)

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Acknowledgments

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.

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  1. Institute of Information Technology, Technical University of Lodz, Wolczanska 215, 90-924, Lodz, Poland

    Arkadiusz Tomczyk & Piotr S. Szczepaniak

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  1. Arkadiusz Tomczyk
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  2. Piotr S. Szczepaniak
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Correspondence to Arkadiusz Tomczyk.

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Tomczyk, A., Szczepaniak, P.S. Adaptive potential active contours. Pattern Anal Applic 14, 425–440 (2011). https://doi.org/10.1007/s10044-011-0200-7

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