Skip to main content

Alternate Spaces For Model Deformation: Application Of Stop And Go Active Models To Medical Images

  • Chapter
Deformable Models

The role of deformable models [1, 2, 3], in medical image analysis [4] has been increasing over the past two decades. The location of a pathology, the study of anatomical structures, computer-assisted surgery, or quantification of tissue volumes are a few of the applications in which deformable models have proved to be very effective. Due to their importance, the study and improvement of these models is still a challenge [5, 6, 7, 8]. These techniques are used to give a highlevel interpretation of low-level information such as contours or isolated regions.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

9 References

  1. Kass M, Witkin A, Terzopoulos D. 1988. Snakes: active contour models. Int J Comput Vision 1:321-331.

    Article  Google Scholar 

  2. Caselles V, Catte F, Coll T, Dibos F. 1993. A geometric model for actives contours. Num Math 66:1-31.

    Article  MATH  MathSciNet  Google Scholar 

  3. Cohen LD. On active contour models and ballons. Comput Vision Graphics Image Process: Image Understand 53(2):211-218, 1991.

    MATH  Google Scholar 

  4. McInerney T, Terzopoulos D. 1996. Deformable models in medical images analysis: a survey. Med Image Anal 1(2):91-108.

    Article  Google Scholar 

  5. Paragios N, Deriche R. 1999. Geodesic active contours for supervised texture segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, Vol. 2, pp. 422-427. Washington, DC: IEEE Computer Society.

    Google Scholar 

  6. Pujol O, Radeva P. 2004. Texture segmentation by statistic deformable models. Int J Image Graphics 4(3):433-452.

    Article  Google Scholar 

  7. Jehan-Besson S, Barlaud M, Aubert G. 2003. DREAMS: deformable regions driven by an Eulerian accurate minimization method for image and video segmentation. Submitted to Int J Comput Vision.

    Google Scholar 

  8. Pujol O, Gil D, Radeva P. 2005. Fundamentals of stop and go active models. J Image Vision Computing 23(8):681-691.

    Article  Google Scholar 

  9. McInerney T, Terzopoulos D. 2000. T-Snakes: topology adaptative snakes. Med Image Anal 4:73-91.

    Article  Google Scholar 

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

    Article  MATH  MathSciNet  Google Scholar 

  11. Ronfard R. 1994. Region-based strategies for active contour models. Int J Comput Vision 13(2):229-251.

    Article  Google Scholar 

  12. Zhu SC. 1994. Region competition: unifying snakes, region growing, and Bayes/MDL for multi-band image segmentation. Technical Report 94-10, Harvard Robotics Laboratory.

    Google Scholar 

  13. Yezzi A, Tsai A, Willsky A. 1999. A statistical approach to snakes for bimodal and trimodal imagery. In Proceedings of the seventh IEEE international conference on computer vision, pp. 898-903. Washington, DC: IEEE Computer Society.

    Chapter  Google Scholar 

  14. Chakraborty A, Staib L, Duncan J. 1996. Deformable boundary finding in medical images by integrating gradient and region information. IEEE Trans Med Imaging 15:859-870.

    Article  Google Scholar 

  15. Samson C, Blanc-F éraud L, Aubert G, Zerubia J. 1999. A level set model for image classification. In Proceedings of the second international conference on scale-space theories in computer vision. Lecture notes in computer science, Vol. 1682, pp. 306-317.

    Chapter  Google Scholar 

  16. Xu C, Yezzi A, Prince J. 2000. On the relationship between parametric and geometric active contours. In Proceedings of the 34th Asilomar conference on signals, systems, and computers, pp. 483-489. Washington, DC: IEEE Computer Society.

    Google Scholar 

  17. Evans LC. 1993. Partial differential equations. Berkeley Mathematics Lecture Notes, vol. 3B. Berkeley: UCB Press.

    Google Scholar 

  18. Tveito A, Winther R. 1998. Introduction to partial differential equations. Texts in Applied Mathematics, no 29. New York: Springer.

    Google Scholar 

  19. Xu C, Prince JL. 1998. Generalized gradient vector flow: external forces for active contours. Signal Process Int J 71(2):132-139.

    Google Scholar 

  20. Gil D, Radeva P. 2005. Curvature vector flow to assure convergent deformable models. Comput Vision Graphics Image Process 99(1):118-125. EMMCVP’03.

    Google Scholar 

  21. Haralick R, Shanmugam K, Dinstein I. 1973. Textural features for image classification. IEEE Trans Syst Mach Cybern 3:610-621.

    Article  Google Scholar 

  22. Tuceryan M. 1994. Moment based texture segmentation. Pattern Recognit Lett 15:659-668.

    Article  Google Scholar 

  23. Lindeberg T. 1994. Scale-space theory in computer vision. New York: Kluwer Academic.

    Google Scholar 

  24. Jain A, Farrokhnia F. 1990. Unsupervised texture segmentation using gabor filters. Proceedings of the international conference on systems, man and cybernetics, pp. 14-19. Washington, DC: IEEE Computer Society.

    Google Scholar 

  25. Mallat S. 1989. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Machine Intell 11(7):674-694.

    Article  MATH  Google Scholar 

  26. Mandelbrot B. 1983. The fractal geometry of nature. New York: W.H. Freeman.

    Google Scholar 

  27. Ojala T, Pietikainen M, Maenpaa T. 2002. Multiresolution grayscale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Machine Intell 24(7):971-987.

    Article  Google Scholar 

  28. Julesz B. 1962. Visual pattern discrimination. IRE Trans Inf Theory, 8:84-92.

    Article  Google Scholar 

  29. P. Ohanian, Dubes R. 1992. Performance evaluation for four classes of textural features. Pattern Recognit 25(8):819-833.

    Article  Google Scholar 

  30. Salembier P, Garrido L. 2000. Binary partition tree as an efficient representation for image processing, segmentation, and information retrieval. IEEE Trans Image Process 9(4):561-576.

    Article  Google Scholar 

  31. Duda R, Hart P. 2001. Pattern classification, 2d ed. New York: Wiley-Interscience.

    Google Scholar 

  32. Paragios N, Deriche R. 1999. Unifying boundary and region-based information for geodesic active tracking. In Proceedings of the IEEE conference on computer vision and pattern recog- nition, Vol. 2, pp. 300-305. Washington, DC: IEEE Computer Society.

    Google Scholar 

  33. Pujo O. A semi-supervised statistical framework and generative snakes for IVUS analysis. PhD dissertation, Universidad Aut ónoma de Barcelona.

    Google Scholar 

  34. Hofmann T, Puzicha J, Buhmann JM. 1998. Unsupervised texture segmentation in a determin- istic annealing framework. IEEE Trans Pattern Anal Machine Intell 20(8):803-818.

    Article  Google Scholar 

  35. Sapiro G. 1997. Color snakes. Comput Vision Image Understand 68(2):247-253.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Pujol, O., Radeva, P. (2007). Alternate Spaces For Model Deformation: Application Of Stop And Go Active Models To Medical Images. In: Deformable Models. Topics in Biomedical Engineering. International Book Series. Springer, New York, NY. https://doi.org/10.1007/978-0-387-68343-0_9

Download citation

  • DOI: https://doi.org/10.1007/978-0-387-68343-0_9

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-31204-0

  • Online ISBN: 978-0-387-68343-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics