Advertisement

Deformable Image Registration with Automatic Non-Correspondence Detection

  • Kanglin Chen
  • Alexander Derksen
  • Stefan Heldmann
  • Marc Hallmann
  • Benjamin Berkels
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9087)

Abstract

Image registration aims at establishing pointwise correspondences between given images. However, in many practical applications, no correspondences can be established in certain parts of the images. A typical example is the tumor resection area in pre- and post-operative medical images. In this paper, we introduce a novel variational framework that combines registration with an automatic detection of non-correspondence regions. The formulation of the proposed approach is simple but efficient, and compatible with a large class of image registration similarity measures and regularizers. The resulting minimization problem is solved numerically with a non-alternating gradient flow scheme. Furthermore, the method is validated on synthetic data as well as axial slices of pre-, post- and intra-operative MR T1 head scans.

Keywords

Joint method Image registration Segmentation Level set Non-correspondence detection Lesion Resection 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Richard, F.J.: A new approach for the registration of images with inconsistent differences. In: 2004 Proceedings of the 17th International Conference on Pattern Recognition. ICPR 2004. vol. 4, pp. 649–652. IEEE (2004)Google Scholar
  2. 2.
    Kim, J., Fessler, J.A.: Intensity-based image registration using robust correlation coefficients. IEEE Transactions on Medical Imaging 23(11), 1430–1444 (2004)CrossRefGoogle Scholar
  3. 3.
    Lu, C., Chelikani, S., Duncan, J.S.: A unified framework for joint segmentation, nonrigid registration and tumor detection: application to MR-guided radiotherapy. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 525–537. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  4. 4.
    Brett, M., Leff, A.P., Rorden, C., Ashburner, J.: Spatial normalization of brain images with focal lesions using cost function masking. Neuroimage 14(2), 486–500 (2001)CrossRefGoogle Scholar
  5. 5.
    Hachama, M., Desolneux, A., Richard, F.J.: Bayesian technique for image classifying registration. IEEE Transactions on Image Processing 21(9), 4080–4091 (2012)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Chitphakdithai, N., Duncan, J.S.: Non-rigid registration with missing correspondences in preoperative and postresection brain images. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part I. LNCS, vol. 6361, pp. 367–374. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  7. 7.
    Zacharaki, E.I., Shen, D., Lee, S.-K., Davatzikos, C.: Orbit: a multiresolution framework for deformable registration of brain tumor images. IEEE Transactions on Medical Imaging 27(8), 1003–1017 (2008)CrossRefGoogle Scholar
  8. 8.
    Gooya, A., Pohl, K.M., Bilello, M., Biros, G., Davatzikos, C.: Joint segmentation and deformable registration of brain scans guided by a tumor growth model. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part II. LNCS, vol. 6892, pp. 532–540. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  9. 9.
    Parisot, S., Duffau, H., Chemouny, S., Paragios, N.: Joint tumor segmentation and dense deformable registration of brain MR images. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part II. LNCS, vol. 7511, pp. 651–658. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  10. 10.
    Kwon, D., Niethammer, M., Akbari, H., Bilello, M., Davatzikos, C., Pohl, K.: Portr: Pre-operative and post-recurrence brain tumor registration. IEEE Transactions on Medical Imaging 33(3), 651–667 (2014)CrossRefGoogle Scholar
  11. 11.
    Ou, Y., Sotiras, A., Paragios, N., Davatzikos, C.: Dramms: Deformable registration via attribute matching and mutual-saliency weighting. Medical image analysis 15(4), 622–639 (2011)CrossRefGoogle Scholar
  12. 12.
    Mumford, D., Shah, J.: Optimal approximation by piecewise smooth functions and associated variational problems. Communications on Pure and Applied Mathematics 42(5), 577–685 (1989)CrossRefzbMATHMathSciNetGoogle Scholar
  13. 13.
    Nikolova, M., Esedoḡlu, S., Chan, T.F.: Algorithms for finding global minimizers of image segmentation and denoising models. SIAM Journal on Applied Mathematics 66(5), 1632–1648 (2006)CrossRefzbMATHMathSciNetGoogle Scholar
  14. 14.
    Berkels, B.: An Unconstrained Multiphase Thresholding Approach for Image Segmentation. In: Tai, X.-C., Mørken, K., Lysaker, M., Lie, K.-A. (eds.) SSVM 2009. LNCS, vol. 5567, pp. 26–37. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  15. 15.
    Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Transactions on Image Processing 10(2), 266–277 (2001)CrossRefzbMATHGoogle Scholar
  16. 16.
    Fischer, B., Modersitzki, J.: Curvature based image registration. Journal of Mathematical Imaging and Vision 18(1), 81–85 (2003)CrossRefzbMATHMathSciNetGoogle Scholar
  17. 17.
    Modersitzki, J.: FAIR: flexible algorithms for image registration. SIAM, vol. 6 (2009)Google Scholar
  18. 18.
    Sundaramoorthi, G., Yezzi, A., Mennucci, A.: Sobolev active contours. International Journal of Computer Vision. 73(3), 345–366 (2007)CrossRefGoogle Scholar
  19. 19.
    Wright, S., Nocedal, J.: Numerical optimization, vol. 2. Springer, New York (1999)zbMATHGoogle Scholar
  20. 20.
    Marx, M.L., Larsen, R.J.: Introduction to mathematical statistics and its applications. Pearson/Prentice Hall (2006)Google Scholar
  21. 21.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International journal of computer vision 1(4), 321–331 (1988)CrossRefGoogle Scholar
  22. 22.
    Mercier, L., Del Maestro, R.F., Petrecca, K., Araujo, D., Haegelen, C., Collins, D.L.: Online database of clinical mr and ultrasound images of brain tumors. Medical physics 39(6), 3253–3261 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Kanglin Chen
    • 1
  • Alexander Derksen
    • 1
  • Stefan Heldmann
    • 1
  • Marc Hallmann
    • 1
  • Benjamin Berkels
    • 2
  1. 1.Fraunhofer MEVIS Project Group Image RegistrationLübeckGermany
  2. 2.AICESRWTH Aachen UniversityAachenGermany

Personalised recommendations