Skip to main content
SpringerLink
Log in

Using Prior Shape and Points in Medical Image Segmentation

  • Conference paper
Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2683))

Abstract

In this paper we present a new variational framework in level set form for image segmentation, which incorporates both a prior shape and prior fixed locations of a small number of points. The idea underlying the model is the creation of two energy terms in the energy function for the geodesic active contours. The first energy term is for the shape, the second for the locations of the points In this model, segmentation is achieved through a registration technique, which combines a rigid transformation and a local deformation. The rigid transformation is determined explicitly by using shape information, while the local deformation is determined implicitly by using image gradients and prior locations. We report experimental results on both synthetic and ultrasound images. These results compared with the results obtained by using a previously reported model, which only incorporates a shape prior into the active contours.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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.

References

  1. Aubert, G., Blanc-Feraud, L.: An element proof of the equivalence between 2D and 3D classical snakes and geodesic active active contours. In: INRIA Rapport de Recherche, Janvier 1998, p. 3340 (1998)

    Google Scholar 

  2. Aubert, G., Vese, L.: A variational method in image recovery. SIAM J. Num. Anal. 34(5), 1948–1979 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  3. Cootes, T., Beeston, C., Edwards, G., Taylor, C.: Unified framework for atlas matching using active appearance models. In: Int’l Conf. Inf. Proc. in Med. Imaging, pp. 322–333. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  4. Chen, Y., Tagare, H., Thiruvenkadam, S.R., Huang, F., Wilson, D., Geiser, A., Gopinath, K., Briggs, R.: Using prior shapes in geometric active contours in a variational framework. International Journal of Computer Vision 50(3), 315–328 (2002)

    Article  MATH  Google Scholar 

  5. Caselles, V., Catté, F., Coll, T., Dibos, F.: A geometric model for active contours in image processing. Numerische Mathematik 66, 1–31 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  6. Caselles, V., Kimmel, R., Sapiro, G.: On geodesic active contours. Intel. Journal of Computer Vision 22(1), 61–79 (1997)

    Article  MATH  Google Scholar 

  7. Cootes, T., Hill, A., Taylor, C., Haslam, J.: The use of active shape models for locating structures in medical images. Image Vision Comput. 13(6), 255–366 (1994)

    Google Scholar 

  8. Chakraborty, A., Staib, H., Duncan, J.: Deformable boundary finding in medical images by integrating gradient and region information. IEEE Transactions on Medical Imaging 15(6), 859–870 (1996)

    Article  Google Scholar 

  9. Cremers, D., Tischhauser, F., Weickert, J., Schnorr, C.: Diffusion-snakes: Introducing statistical shape knowledge into the Mumford-Shah functional. International Journal of Computer Vision 50(3), 295–315 (2002)

    Article  MATH  Google Scholar 

  10. Cootes, T., Taylor, C., Cooper, D., Graham, J.: Active shape model - their training and application. Computer Vision and Image Understanding 61, 38–59 (1995)

    Article  Google Scholar 

  11. Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Processing 10(2), 266–277 (2001)

    Article  MATH  Google Scholar 

  12. Dias, J., Leitao, J.: Wall position and thickness estimation from sequences of echocardiograms images. IEEE Trans. Med. Imag. 15, 25–38 (1996)

    Article  Google Scholar 

  13. Giusti, E.: Minimal Surfaces and Functions of Bounded Variation. Birkhauser, Basel (1985)

    Google Scholar 

  14. Staib, L.H., Duncan, J.S.: Deformable Fourier models for surface finding in 3D images. In: Robb, R.A. (ed.) Second Conf. on Visualization in Biomedical Computing(VBC 1992), Chapel Hill, NC, vol. 1808, pp. 90–104. SPIE, Bellingham (1992)

    Google Scholar 

  15. Leventon, M.E., Grimson, W.E.L., Faugeras, O.: Statistical Shape Influence in Geodesic Active Contours. In: Proc. IEEE Conf. Comp. Vision and Patt. Recog., pp. 316–323 (2000)

    Google Scholar 

  16. Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 1, 321–331 (1988)

    Article  Google Scholar 

  17. Kichenassamy, S., Kumar, A., Olver, P., Tannenbaum, A., Yezzi, A.J.: Gradient flows and geometric active contour models. In: Proc. ICCV 1995, pp. 810–815. IEEE Computer Soc. Press, Cambridge (1995)

    Google Scholar 

  18. McInerney, T., Terzopoulos, D.: Deformable models in medical image analysis: a survey. Medical Image Analysis 1(2), 91–108 (1996)

    Article  Google Scholar 

  19. Mumford, D., Shah, J.: Optimal approximation by piecewise smooth functions and associated variational problems. Comm. Pure Appl. Math. 42, 557–685 (1989)

    Article  MathSciNet  Google Scholar 

  20. Malladi, R., Sethian, J., Vemuri, B.: Shape modeling with front propagation: A level set approach. IEEE Trans. Pattern Anal. machine Intell. 17, 158–175 (1995)

    Article  Google Scholar 

  21. Metaxas, D., Terzopoulos, D.: Shape and nonrigid motion estimation through physics-based synthesis. IEEE trans. Pattern Anal. Machine Intelligence 15(6), 580–591 (1993)

    Article  Google Scholar 

  22. McInerney, T., Terzopoulos, D.: Deformable models in medical image analysis: a survey. Medical Image Analysis 1(2), 91–108 (1996)

    Article  Google Scholar 

  23. Osher, S., Sethian, J.A.: Fronts propagating with curvature-dependent speed: algorithm based on Hamilton-Jacobi formulation. Journal of Computational Physics 70, 12–49 (1988)

    Article  MathSciNet  Google Scholar 

  24. Paragios, N.: User-interactive level set method for image segmentation (submitted to ICCV 2003)

    Google Scholar 

  25. Paragios, N., Deriche, R.: Geodesic active regions for supervised texture segmentation. In: ICCV-WS 1999. LNCS, vol. 1883. Springer, Heidelberg (2000)

    Google Scholar 

  26. Paragios, N.: Geodesic active regions and level set methods: contributions and applications in artificial vision. Ph.D. thesis, School of Computer Engineering, University of Nice/Sophia Antipolis (2000)

    Google Scholar 

  27. Paragios, N.: A variational approach for the segmentation of the left ventricle in MR cardiac images. In: Proceedings 1st IEEE Workshop on Variational and Level Set methods in Computer Vision, Vancouver, B.C., Canada, 13 July 2001, pp. 153–160 (2001)

    Google Scholar 

  28. Paragios, N., Rousson, M.: Shape prior for level set representations. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 78–92. Springer, Heidelberg (2002)

    Google Scholar 

  29. Paragios, N., Rousson, M., Ramesh, V.: Marching distance functions: a shape-to-area variational approach for global-to-local registration. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 775–789. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  30. Richard, F., Cohen, L.: A New Image Registration Technique with Free Boundary Constraints: Application to Mammography. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 531–545. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  31. Staib, L., Duncan, J.: Boundary finding with parametrically deformable contour methods. IEEE Trans. Patt. Analysis and Mach. Intell. 14(11), 1061–1075 (1992)

    Article  Google Scholar 

  32. Szekel, G., Kelemen, A., Brechbuhler, C., Gerig, G.: Segmentation of 2-D and 3-D objects from MRI volume data using constrained elastic deformation of flexible Fourier surface models. Medical Image Analysis 1(1), 19–34 (1996)

    Google Scholar 

  33. Sussman, M., Smereka, P., Osher, S.: A level set approach for computing solutions to incompressible two phase flow. J. Comput. Phys. 119, 146–159 (1994)

    Article  Google Scholar 

  34. Soatto, S., Yezzi, A.: Deformation: deforming motion, shape average and joint registration and segmentation of images. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 32–47. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  35. Tagare, H.D.: Deformable 2-D Template Matching Using implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification. IEEE Transactions on Image Processing 10(8), 1169–1186 (2001)

    Article  Google Scholar 

  36. Tsai, A., Yezzi Jr., A., Willsky, A.S.: Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification. IEEE Transactions on Image Processing 10(8), 1169–1186 (2001)

    Article  MATH  Google Scholar 

  37. Vemuri, B.C., Chen, Y., Wang, Z.: registration associated image smoothing and segmentation. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 546–559. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  38. Vemuri, B.C., Radisavljevie, A.: Multiresolution stochastic hybrid shape models with fractal priors. ACM Trans. on Graphics 13(2), 177–207 (1994)

    Article  MATH  Google Scholar 

  39. Wang, Y., Staih, L.: Boundary funding with corresponding using statistical shape models. In: Proc. IEEE Conf. Comp. Vision and Patt. Recog., pp. 338–345 (1998)

    Google Scholar 

  40. Yuille, A., Hallinan, P.W., Cohen, D.S.: Feature extraction from faces using deformable templates. J. Computer Vision 8, 99–111 (1992)

    Article  Google Scholar 

  41. Yezzi, A., Kichenassamy, S., Kumar, A., Olver, P.J., Tannenbaum, A.: A geometric snake model for segmentation of medical imagery. IEEE Trans. Medical Imaging 16, 199–209 (1997)

    Article  Google Scholar 

  42. Zhao, H.K., Chan, T., Merriman, B., Osher, S.: A variational level set approach to multiphase motion. J. Comput. Phys. 127, 179–195 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  43. Zhu, S.C., Yuille, A.: Region Competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation. IEEE PAMI 18, 884–900 (1996)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics, University of Florida, Gainesville, 32611, FL.

    Yunmei Chen, Weihong Guo, Feng Huang & David Wilson

  2. Division of Cardiology, Department of Medicine, University of Florida, Gainesville, 32611, FL.

    Edward A. Geiser

Authors
  1. Yunmei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weihong Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Feng Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. David Wilson
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Edward A. Geiser
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of CISE, University of Florida, Gainesville, FL, USA

    Anand Rangarajan

  2. Instituto de Telecomunicações, Instituto Superior Técnico, T.U. Lisbon, Av. Rovisco Pais, 1049-001, Lisboa, Portugal

    Mário Figueiredo

  3. ARIANA Research Group, INRIA/I3S, Sophia Antipolis, France

    Josiane Zerubia

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Chen, Y., Guo, W., Huang, F., Wilson, D., Geiser, E.A. (2003). Using Prior Shape and Points in Medical Image Segmentation. In: Rangarajan, A., Figueiredo, M., Zerubia, J. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2003. Lecture Notes in Computer Science, vol 2683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45063-4_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-45063-4_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40498-9

  • Online ISBN: 978-3-540-45063-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation