Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
SpringerLink
Log in

On using an analogy to heat flow for shape extraction

  • Theoretical Advances
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

We introduce a novel evolution-based segmentation algorithm which uses the heat flow analogy to gain practical advantage. The proposed algorithm consists of two parts. In the first part, we represent a particular heat conduction problem in the image domain to roughly segment the region of interest. Then we use geometric heat flow to complete the segmentation, by smoothing extracted boundaries and removing noise inside the prior segmented region. The proposed algorithm is compared with active contour models and is tested on synthetic and medical images. Experimental results indicate that our approach works well in noisy conditions without pre-processing. It can detect multiple objects simultaneously. It is also computationally more efficient and easier to control and implement in comparison with active contour models.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Kass M, Witkin A, Terzopoulos D (1987) Snakes: active contour models. Int J Comput Vision 321–331

  2. Xu C, Prince JL (1998) Snakes, shapes and gradient vector flow. IEEE Trans Image Process 7(3):359–369

    Article  MathSciNet  MATH  Google Scholar 

  3. Li B, Acton ST (2007) Active contour external force using vector field convolution for image segmentation. IEEE Trans Image Process 16(8):2096–2106

    Article  MathSciNet  Google Scholar 

  4. Osher S, Sethian JA (1988) Fronts propagating with curvature-dependent speed: algorithms based on Hamilton–Jacobi formulations. J Comput Phys 79(1):12–49

    Article  MathSciNet  MATH  Google Scholar 

  5. Caselles V, Catte F, Coll T, Dibos F (1993) A geometric model for active contours. Numerische Mathematic 66:1–31

    Article  MathSciNet  MATH  Google Scholar 

  6. 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 

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

    Article  MATH  Google Scholar 

  8. Siddiqi K, Lauziere Y, Tannenbaum A, Zucker S (1998) Area and length minimizing flows for shape segmentation. IEEE Trans Image Process 7(3):433–443

    Article  Google Scholar 

  9. Xie X, Mirmehdi M (2004) RAGS: region-aided geometric snake. IEEE Trans Image Process 13(5):640–652

    Article  MathSciNet  Google Scholar 

  10. Adalsteinsson D, Sethian J (1995) A fast level set method for propagating interfaces. J Comput Phys 118(2):269–277

    Article  MathSciNet  MATH  Google Scholar 

  11. Sethian J (1999) Level set methods and fast marching methods. Cambridge Univ Press, New York

    MATH  Google Scholar 

  12. Weickert J, Romeny BMTH, Viergever MA (1998) Efficient and reliable schemes for nonlinear diffusion filtering. IEEE Trans Image Process 7(3):398–410

    Article  Google Scholar 

  13. Chan T, Vese L (2001) Active Contours without Edges. IEEE Trans Image Process 10(2):266–277

    Article  MATH  Google Scholar 

  14. Mumford D, Shah J (1989) Optimal approximation by piecewise smooth functions and associated variational problems. Commun Pure Appl Math 42(4):577–685

    Article  MathSciNet  MATH  Google Scholar 

  15. Vese L, Chan T (2002) A multiphase level set framework for image segmentation using the Mumford and Shah Model. Int J Comput Vision 50:271–293

    Article  MATH  Google Scholar 

  16. Tsai A, Yezzi A, Willsky AS (2001) Curve evolution implementation of the mumford-shah functional for image segmentation, denoising, interpolation, and magnification. IEEE Trans Image Process 10(8):1169–1186

    Article  MATH  Google Scholar 

  17. Li C, Kao C, Gore J, Ding Z (2007) Implicit active contours driven by local binary fitting energy. IEEE IntConf Comput Vision Pattern Recognit 1–7

  18. Li C, Kao C, Gore J, Ding Z (2008) Minimization of region-scalable fitting energy for image segmentation. IEEE Trans Image Process 17(10):1940–1949

    Article  MathSciNet  Google Scholar 

  19. Zhang K, Song H, Zhang L (2010) Active contours driven by local image fitting energy. Pattern Recognit 43:1199–1206

    Article  MATH  Google Scholar 

  20. Zimmer C, Olivo-Marin JC (2005) Coupled parametric active contours. IEEE Trans Pattern Anal Mach Intell 27(11):1838–1842

    Article  Google Scholar 

  21. Paragios N, Deriche R (2000) Coupled geodesic active regions for image segmentation: a level set approach. Eur Conf Comput Vision 224–240

  22. Xie X, Mirmehdi M (2008) MAC: magnetostatic active contour model. IEEE Trans Pattern Anal Mach Intell 30(4):632–646

    Article  Google Scholar 

  23. Huang X, Metaxas D (2008) Metamorphs: deformable shape and appearance models. IEEE Trans Patt Anal Mach Intell 30(8):1444–1459

    Article  Google Scholar 

  24. Adams R, Bischof L (1994) Seeded region growing. IEEE Trans Pattern Anal Mach Intell 16(6):641–647

    Article  Google Scholar 

  25. Fung PW, Grebbin G, Attikiouzel Y (1990) Model-based region growing segmentation of textured images. IEEE Int Conf Acoust Speech Signal Process 4:2313–2316

    Article  Google Scholar 

  26. Xiaohan Y, Yla-Jailski J, Huttunen O, Vehkomiiki T, Sipila O, Katila T (1992) Image segmentation combining region growing and edge detection. Int Conf Pattern Recognit 3:481–484

    Google Scholar 

  27. Yu Q, Clausi DA (2008) IRGS: image segmentation using edge penalties and region growing. IEEE Trans Pattern Anal Mach Intell 30(12):2126–2139

    Article  Google Scholar 

  28. Qin AK, Clausi DA (2010) Multivariate image segmentation using semantic region growing with adaptive edge penalty. IEEE Trans Image Process 19(8):2157–2170

    Article  MathSciNet  Google Scholar 

  29. Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 22(8):629–639

    Article  Google Scholar 

  30. Manay S, Yezzi A (2003) Anti-geometric diffusion for adaptive thresholding and fast segmentation. IEEE Trans Image Process 12(11):1310–1323

    Article  MathSciNet  Google Scholar 

  31. Ji X, Feng J (2004) A new approach to thinning based on time-reversed heat conduction model. IEEE Int Conf Image Process 1:653–656

    Google Scholar 

  32. Kimia BB, Siddiqi K (1994) Geometric heat equation and nonlinear diffusion of shapes and images. IEEE Int Conf Comput Vision Pattern Recognit 113–120

  33. Makrogiannis SK, Bourbakis NG (2004) Motion analysis with application to assistive vision technology. IEEE Int Conf Tools Artif Intell 344–352

  34. Direkoğlu C, Nixon MS (2006) Low level moving-feature extraction via heat flow analogy. Int Symp Visual Comput LNCS 4291:243–252

    Google Scholar 

  35. Direkoğlu C, Nixon MS (2010) Moving-edge detection via heat flow analogy. Pattern Recognit Lett (in press)

  36. Direkoğlu C, Nixon MS (2007) Shape extraction via heat flow analogy. Int Conf Adv Concepts Intell Vision LNCS 4678:553–564

    Google Scholar 

  37. Nixon MS, Liu XU, Direkoğlu C, Hurley DJ (2011) On using physical analogies for feature and shape extraction in computer vision. Comput J 54(1):11–25

    Google Scholar 

  38. Holman JP (2002) Heat transfer, McGraw-Hill, 9th edn

  39. Wasilewski M (2002) Active contours without edges matlab codes. http://www.postulate.org/segmentation.php

  40. Li C, Kao C, Gore J, Ding Z (2008) Region-scalable fitting energy based active contour matlab codes. http://www.engr.uconn.edu/~cmli/

  41. Xu C, Prince JL (1999) Gradient vector flow snake matlab codes. http://iacl.ece.jhu.edu/projects/gvf/

Download references

Author information

Authors and Affiliations

  1. School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK

    Cem Direkoğlu & Mark S. Nixon

Authors
  1. Cem Direkoğlu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mark S. Nixon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cem Direkoğlu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Direkoğlu, C., Nixon, M.S. On using an analogy to heat flow for shape extraction. Pattern Anal Applic 16, 125–139 (2013). https://doi.org/10.1007/s10044-011-0223-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-011-0223-0

Keywords

Search

Navigation