Skip to main content

Advertisement

SpringerLink
Log in

Spatially adaptive active contours: a semi-automatic tumor segmentation framework

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Purpose

Tumor segmentation constitutes a crucial step in simulating cancer growth and response to therapy. Incorporation of imaging data individualizes the simulation and assists clinical correlation with the predicted outcome. We adapted snakes to improve tumor segmentation including difficult cases with inherently inhomogeneous structure and poorly defined margins.

Methods

Snakes are flexible curves, based on the parameter-controlled deformation of an initial user-defined contour toward the boundary of the desired object, through the minimization of a suitable energy function. Although parameter-adjustment can yield fairly good results in homogeneous regions, traditional snakes often fail to provide an accurate segmentation result when both rigid and very elastic behavior is needed simultaneously to delineate the true outline of the tumor. We developed and tested a spatially adaptive active contour technique by introducing local snake bending, to improve traditional snakes performance for segmenting tumors. The key point of our method is the use of adaptable snake parameters, instead of constant ones, to adjust the bending of the curve according to the local edge characteristics. Our algorithm discriminates image regions according to underlying image features, such as gradient magnitude and corner strength. More specifically, it assigns each region a different “localized” set of parameters, one corresponding to a very flexible snake, and the other corresponding to a very rigid one, according to the local image characteristics.

Results

Qualitative results on more than 150 real MR images, as well as quantitative validation based on agreement with an expert clinician’s annotations of the true tumor boundaries, demonstrate our approach is highly efficient compared to traditional active contours and region growing. Due to the use of adaptable parameters in the snake evolution process, our approach outperforms the other two methods, and consistently follows an expert’s annotations. Statistical tests indicated significant difference between the results produced by our approach and two other algorithms traditional snakes and region growing, while multiple comparison showed that our method consistently outperformed those algorithms, with an average overlap of 89%, over the entire data set, while traditional snakes were at 82.5% and region growing at 59.2%. Furthermore, we performed several tests that demonstrate our method’s stability to different initial contours, as well as, to lower resolution images.

Conclusion

Our adaptive snake algorithm can spatially adapt to diverse image characteristics, producing outlines that mimic the true tumor boundaries. Results in MR datasets are very close to an expert clinician’s intuition about the tumor boundaries.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Casciari J, Sotirchos S, Sutherland R (1992) Variations in tumor growth rates and metabolism with oxygen concentration, glucose concentration, and extracellular pH. J Cell Physiol 151: 386–394

    Article  CAS  PubMed  Google Scholar 

  2. Santini M, Rainaldi G (1999) Three-dimensional spheroid model in tumor biology. Pathobiology 67: 148–157

    Article  CAS  PubMed  Google Scholar 

  3. Wiarda D, Travis C (1997) Determinability of model parameters in a two-stage deterministic cancer model. Math Biosci 146: 1–13. doi:10.1016/S0025-5564(97)00025-4

    Article  CAS  PubMed  Google Scholar 

  4. Stamatakos G, Antipas V, Uzunoglu U (2006) A spatiotemporal, patient individualized simulation model of solid tumor response to chemotherapy in vivo: the paradigm of glioblastoma multi- forme treated by temozolomide. IEEE Trans Biomed Eng 53: 1467–1477

    Article  PubMed  Google Scholar 

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

    Article  Google Scholar 

  6. Beucher S (1991) The watershed transformation applied to image segmentation. In: Proceedings of scanning microscopy international, pp 299–314

  7. Boykov Y, Jolly M (2001) Interactive graph cuts for optimal boundary and region segmentation of objects in ND images. In: International conference on computer vision, pp 105–112

  8. Barrett W, Mortensen E (1997) Interactive live-wire boundary extraction. Med Image Anal 1: 331–341

    Article  CAS  PubMed  Google Scholar 

  9. Kass M, Witkin A, Terzopoulos D (1998) Snakes: active contour models. Int J Comput Vis 1: 321–331

    Article  Google Scholar 

  10. Xu C, Prince J (1997) Gradient vector flow: a new external force for snakes. In: Proceedings of 1997 IEEE computer society conference on computer vision and pattern recognition, pp 66–71

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  13. Kim D, Park J (2004) Computer aided detection of kidney tumor on abdominal computed tomography scans. Acta Radiol 45: 791–795

    Article  CAS  PubMed  Google Scholar 

  14. Mancas M, Gosselin B (2003) Fuzzy tumor segmentation based on iterative watersheds. In: Proceedings of 14th ProRISC workshop circuits systems and signal process. Unpublished

  15. Boykov Y, Jolly M (2000) Interactive organ segmentation using graph cuts. In: Medical image computing and computer-assisted intervention, pp 276–286

  16. Boykov Y, Funka-Lea G (2006) Graph cuts and efficient nd image segmentation. Int J Comput Vis 70: 109–131

    Article  Google Scholar 

  17. Esneault S, Hraiech N, Delabrousse E, Dillenseger J (2007) Graph cut liver segmentation for interstitial ultrasound therapy. Conf Proc IEEE Eng Med Biol Soc 1: 5247–5250

    Google Scholar 

  18. Cohen L, Cohen I, Ceremade P (1993) Finite-element methods for active contour models and balloons for 2-D and 3-D images. IEEE Trans Pattern Anal Mach Intell 15: 1131–1147

    Article  Google Scholar 

  19. Nogueira S, Ruichek Y (2007) A new B-spline based active contour approach. In: IAPR conference on machine vision applications, pp 268–271

  20. Lu R, Marziliano P, Thng C (2005) Liver tumor volume estimation bye semi-automatic segmentation method. In: Proceedings of 2005 IEEE engineering in medicine and biology 27th Annual Conference, pp 3296–3299

  21. Cvancarova M, Albregtsen F, Brabrand K, Samset E (2005) Segmentation of ultrasound images of liver tumors applying snake algorithms and GVF. In: International congress of computer assisted radiology and surgery

  22. Gu L, Xu J, Peters T (2006) Novel multistage three-dimensional medical image segmentation: methodology and validation. IEEE Trans Inf Technol Biomed 10: 740–748

    Article  PubMed  Google Scholar 

  23. Linguraru M et al (2009) Renal tumor quantification and classification in contrast-enhanced abdominal CT. Pattern Recognit 42: 1149–1161

    Article  PubMed  Google Scholar 

  24. Aleman-Flores M, Álvarez L, Caselles V (2007) Texture-oriented anisotropic filtering and geodesic active contours in breast tumor ultrasound segmentation. J Math Imaging Vis 28: 81–97

    Article  Google Scholar 

  25. Kanakatte A, Gubbi J, Srinivasan B, Mani N, Kron T, Binns D, Palaniswami M (2008) Pulmonary tumor volume delineation in PET images using deformable models. Conf Proc IEEE Eng Med Biol Soc, pp 3118–3121

  26. Farmaki C, Marias K, Sakkalis V, Graf N (2009) A spatially adaptive active contour method for improving semi-automatic medical image annotation. In: Proc World congress on medical physics and biomedical engineering, pp 1878–1881

  27. Logan J (1997) Applied mathematics, chap 3, 2nd edn. Wiley, New york

    Google Scholar 

  28. Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12: 629–639

    Article  Google Scholar 

  29. Tsai D-M, Chao S-M (2005) An anisotropic diffusion-based defect detection for sputtered surfaces with inhomogeneous textures. Image Vis Comput 23: 325–338

    Article  Google Scholar 

  30. Gonzalez RC, Woods RE (2002) Digital image processing. Prentice Hall, UK, pp, pp 134–137

    Google Scholar 

  31. Harris C, Stephens M (1988) A combined corner and edge detector. In: Alvey vision conference, pp 147–152

  32. Chang F, Chen C, Lu C (2004) A linear-time component-labeling algorithm using contour tracing technique. Comput Vis Image Underst 93: 206–220

    Article  Google Scholar 

  33. Zou K et al (2004) Statistical validation of image segmentation quality based on a spatial overlap index: scientific reports. Acad Radiol 11: 178–189

    Article  PubMed  Google Scholar 

  34. Daniel WW (1978) Applied nonparametric statistics. Houghton Mifflin, Boston

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Computer Science, Foundation for Research and Technology, Hellas, 70013, Heraklion, Crete, Greece

    Cristina Farmaki, Konstantinos Marias & Vangelis Sakkalis

  2. Department of Paediatric Oncology and Haematology, University of the Saarland, Homburg, Germany

    Norbert Graf

Authors
  1. Cristina Farmaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Konstantinos Marias
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vangelis Sakkalis
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Norbert Graf
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cristina Farmaki.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Farmaki, C., Marias, K., Sakkalis, V. et al. Spatially adaptive active contours: a semi-automatic tumor segmentation framework. Int J CARS 5, 369–384 (2010). https://doi.org/10.1007/s11548-010-0477-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-010-0477-9

Keywords

Search

Navigation