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3D image segmentation by using statistical deformation models and level sets

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Abstract

Purpose Improved segmentation of soft objects was sought using a new method that combines level set segmentation with statistical deformation models, using prior knowledge of the shape of an object as well as information derived from the input image.

Methods Statistical deformation models were created using Euclidian distance functions of binary data and a multi-hierarchical registration approach based on mutual information metric and demons deformable registration. This approach is motivated by the fact that models based on signed distance maps, traditionally combined with level set segmentation can result in irregular shapes and do not establish explicit correspondences. By using statistical deformation models as representation of shape and a maximum a posteriori (MAP) estimation model to estimate the MAP shape of the object to be segmented, a robust segmentation algorithm using accurate shape models could be developed.

Results The accuracy and correctness of the synthesized models was evaluated on different 3D objects (cardiac MRI and spinal CT vertebral segment) and the segmentation algorithm was validated by performing different segmentation tasks using various image modalities. The results of this evaluation are very promising and show the potential utility of the approach.

Conclusion Initial results demonstrate the approach is feasible and may be advantageous over alternative segmentation methods. Extensions of the model, which also incorporate prior knowledge about the spatial distribution of grey values, are currently under development.

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References

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

    Article  Google Scholar 

  2. Sethian JA (1985) Curvature and evolution of fronts. Commun Math Phys, vol 101

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

    Article  Google Scholar 

  4. Sethian JA (1999) Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision and material science, 2nd edn. Cambridge University Press, Cambridge

    Google Scholar 

  5. Rueckert D, Frangi AF, Schnabel JA (2001) Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration. IEEE Trans Med Imaging 22:1014–1025

    Article  Google Scholar 

  6. Frangi AF, Rueckert D, Schnabel JA, Niessen WJ (2002) Automatic construction of multiple-object three-dimensional statistical shape models: application to cardiac modeling. IEEE Trans Med Imaging 21:1151–66

    Article  PubMed  Google Scholar 

  7. Loeckx D, Maes F, Vandermeulen D, Suetens P (2003) Temporal subtraction of thorax CR images using a statistical deformation model. IEEE Trans Med Imaging 22:1490–1504

    Article  PubMed  CAS  Google Scholar 

  8. Woutersa J, D’Agostino E, Maes F, Vandermeulen D, Suetens P (2006) Non-rigid brain image registration using a statistical deformation model. SPIE Medical Imaging, San Diego (presented)

  9. Baillard C, Hellier P, Barillot C (2001) Segmentation of brain 3D MR images using level sets and dense registration. Med Image Anal 5:185–94

    Article  PubMed  CAS  Google Scholar 

  10. Thirion J-P (1998) Image matching as a diffusion process: an analogy with Maxwell’s demons. Med Image Anal 2:243–260

    Article  PubMed  CAS  Google Scholar 

  11. Leventon M, Grimson E, Faugeras O (2000) Statistical shape influence in geodesic active contours. Comput Vis Pattern Recognit 1:316–323

    Google Scholar 

  12. Caselles V, Kimmel R, Sapiro G (1993) A geometric model for active contours. Numerische Mathematik, vol 66

  13. Caselles V, Kimmel R, Sapiro G (1997) Geodesic active contours. Int J Comput Vis 22(1):61–97

    Article  Google Scholar 

  14. Tsai A, Yezzi A, Wells W, Tempany C, Tucker D, Fan A, Grimson A, Willsky A (2001) Model based curve evolution technique for image segmentation. IEEE Comput Vis Pattern Recognit 1:463–468

    Google Scholar 

  15. Tsai A, Yezzi A, Wells W, Tempany C, Tucker D, Fan A, Grimson A, Willsky A (2003) A shape-based approach to curve evolution for segmentation of medical imagery. IEEE Trans Med Imaging 22:137–154

    Article  PubMed  Google Scholar 

  16. Yang J, Duncan JS (2003) 3D image segmentation of deformable objects with shape-appearance joint prior models. MICCAI, Montreal (presented)

  17. Yang J, Duncan JS (2004) Neighbor-constrained segmentation with 3D deformable models. TMI (to appear)

  18. Yang J, Duncan JS (2004) 3D image segmentation of deformable objects with joint shape-intensity prior models using level sets. Med Image Anal 8:285–294

    Article  PubMed  Google Scholar 

  19. Chan T, Vese L (2001) Active contours without edges. IEEE Trans Image Process 10:266–277

    Article  PubMed  CAS  Google Scholar 

  20. Paragios N (2002) Shape priors for level set representations. European Conference on Computer Vision, Copenhagen (presented)

  21. Cremers D, Schnorr C, Weickert J (2001) Diffusion-snakes: combining statistical shape knowledge and image information in a variational framework. In: IEEE workshop on variational level set methods, pp 137–144

  22. Chen Y, Tagare H, Thiruvenkadam S, Huang F, Wilson DC, Gopinath KS, Briggs RW, Geiser EA (2002) Using prior shapes in geometric active contours in a variational framework. Int J Comput Vis 50:315–328

    Article  Google Scholar 

  23. Huang X, Metaxas DN, Chen T (2004) MetaMorphs: deformable shape and texture models. Computer Vision and Pattern Recognition, Washington DC (presented)

  24. Rousson M, Paragios N, Deriche R (2004) Implicit active shape models for 3D segmentation in MRI imaging. MICCAI, St. Malo (presented)

  25. Danielsson PE (1980) Euclidian distance mapping. Comput Graph Image Process 4:227–248

    Article  Google Scholar 

  26. Borgefors G (1984) Distance transformations in arbitrary dimensions. Comput Vis Graph Image Process 27: 341–345

    Article  Google Scholar 

  27. Golland P, Grimson WEL, Shenton ME, Kikinis R (2005) Detection and analysis of statistical dierences in anatomical shape. Med Image Anal 9:69–86

    Article  PubMed  Google Scholar 

  28. Lapp RM, Lorenzo-Valdés M, Rueckert D (2004) 3D/4D cardiac segmentation using active appearance models, non-rigid registration, and the insight toolkit. In: Lecture notes in computer science, vol 3216, pp 419–426

  29. Cootes TF, Taylor CJ, Cooper DH, Graham J (1995) Active shape models: their training and applications. Comput Vis Image Underst 61:38–59

    Article  Google Scholar 

  30. Maintz JBA, Viergever MA (1998) A survey of medical image registration. Med Image Anal 2:1–36

    Article  PubMed  CAS  Google Scholar 

  31. Collignon A, Maes F, Delaere D, Vandermeulen D, Suetens P, Marchal G (1995) Automated multi modality image registration based on information theory. In: Information processing in medical imaging 1995. Kluwer, pp 263–274

  32. Viola P, Wells WM (1997) Alignment of maximization of mutual information. Int J Comput Vis 22:61–97

    Article  Google Scholar 

  33. Whitaker R (1998) A level-set approach to 3D reconstruction from range data. Int J Comput Vis 29:203–231

    Article  Google Scholar 

  34. Styner M, Gerig G, Brechbuehler Ch, Szekely G (2000) Parametric estimate of intensity inhomogeneities applied to MRI. IEEE Trans Med Imaging 19:153–165

    Article  PubMed  CAS  Google Scholar 

  35. Butt MA, Maragos P (1998) Optimum design of chamfer distance transforms. IEEE Trans Imaging Proc 7: 1477–1485

    Article  Google Scholar 

  36. Krissian K, Westin CF (2003) Fast and accurate redistancing for level set methods. EUROCAST, Las Palmas, Gran Canaria, Spain (presented)

  37. Zijdenbos AP, Dawant BM, Margolin RA, Palmer AC (1994) Morphometric analysis of white matter lesions in MR images: method and validation. IEEE Trans Med Imaging 13: 716–724

    Article  PubMed  CAS  Google Scholar 

  38. Fritscher KD, Pilgram R, Schubert R (2005) Automatic 4D cardiac segmentation using level sets. Functional imaging and modeling of the heart, San Diego, CA (presented)

  39. Fritscher KD, Schubert R (2005) A software framework for pre-processing and level-set segmentation of medical image data. SPIE Medical Imaging, San Diego (presented)

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Authors and Affiliations

  1. Institute for Biomedical Image Analysis, University for Health Sciences, Medical Informatics and Technology, Eduard Wallnöfer-Zentrum 1, 6060, Hall in Tirol, Austria

    Karl D. Fritscher & Rainer Schubert

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  1. Karl D. Fritscher
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  2. Rainer Schubert
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Correspondence to Karl D. Fritscher.

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Fritscher, K.D., Schubert, R. 3D image segmentation by using statistical deformation models and level sets. Int J CARS 1, 123–135 (2006). https://doi.org/10.1007/s11548-006-0048-2

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