Groupwise Registration of MR Brain Images Containing Tumors via Spatially Constrained Low-Rank Based Image Recovery

  • Zhenyu Tang
  • Yue Cui
  • Bo JiangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10434)


We propose a new low-rank based image recovery method and embed it into an existing Groupwise Image Registration (GIR) framework to achieve accurate GIR of Magnetic Resonance (MR) brain images containing tumors. In our method, brain tumor regions in the input images are recovered with population-consistent normal brain appearance to produce low-rank images. The GIR framework is then applied to the tumor-free low-rank images. With no influence from the brain tumor, accurate GIR can be achieved. Unlike conventional low-rank based image recovery methods, a spatial constraint is added to the low-rank framework in our method, by which the quality of the resulting low-rank images can be improved. Particularly, the low-rank images produced by our method contain both effectively recovered brain tumor regions and well-preserved normal brain regions of input images, which are two key factors for accurate GIR. By contrast, in conventional low-rank based image recovery methods, these two factors are mutually exclusive and a good balance is difficult to achieve. Synthetic and real MR brain images are used to evaluate our method. The results show that based on our method, image recovery quality and GIR accuracy are improved in comparison to the state-of-the-art method.



This work was supported by National Natural Science Foundation of China (Nos. 61502002, 61602001) and Natural Science Foundation of Anhui Province Education Department (Nos. KJ2015A008, KJ2016A040, KJ2017A016).


  1. 1.
    Joshi, S., et al.: Unbiased diffeomorphic atlas construction for computational anatomy. Neuroimage 23, S151–S160 (2004)CrossRefGoogle Scholar
  2. 2.
    Iglesias, J., Sabuncu, M.: Multi-atlas segmentation of biomedical images: a survey. Med. Image Anal. 24, 205–219 (2015)CrossRefGoogle Scholar
  3. 3.
    Hamm, J., Davatzikos, C., Verma, R.: Efficient large deformation registration via geodesics on a learned manifold of images. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5761, pp. 680–687. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-04268-3_84CrossRefGoogle Scholar
  4. 4.
    Wu, G., et al.: SharpMean: groupwise registration guided by sharp mean image and tree-based registration. Neuroimage 56, 1968–1981 (2011)CrossRefGoogle Scholar
  5. 5.
    Brett, M., et al.: Spatial normalization of brain images with focal lesions using cost function masking. Neuroimage 14, 486–500 (2001)CrossRefGoogle Scholar
  6. 6.
    Zacharaki, E.I., et al.: ORBIT: a multiresolution framework for deformable registration of brain tumor images. IEEE TMI 27, 1003–1017 (2008)Google Scholar
  7. 7.
    Liu, X., et al.: Low-rank atlas image analyses in the presence of pathologies. IEEE TMI 34, 2583–2591 (2015)Google Scholar
  8. 8.
    Peng, Y., et al.: RASL: robust alignment by sparse and low-rank decomposition for linearly correlated images. IEEE TPAMI 34, 46–2233 (2012)Google Scholar
  9. 9.
    Zhou, X., et al.: Moving object detection by detecting contiguous outliers in the low-rank representation. IEEE TPAMI 35, 597–610 (2013)CrossRefGoogle Scholar
  10. 10.
    Evans, A.C., et al.: An MRI-based stereotactic brain atlas from 300 young normal subjects. In: Proceedings of the 22nd Symposium of the Society for Neuroscience, Anaheim (1992)Google Scholar
  11. 11.
    Mazumder, R., et al.: Spectral regularization algorithms for learning large incomplete matrices. J. Mach. Learn. Res. 11, 2287–2322 (2010)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Boykov, Y., et al.: Fast approximate energy minimization via graph cuts. IEEE TPAMI 23, 1222–1239 (2001)CrossRefGoogle Scholar
  13. 13.
    Shattuck, D.W., et al.: Construction of a 3D probabilistic atlas of human cortical structures. Neuroimage 39, 1064–1080 (2008)CrossRefGoogle Scholar
  14. 14.
    Menze, B., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE TMI 34, 1993–2024 (2015)Google Scholar
  15. 15.
    Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26, 297–302 (1945)CrossRefGoogle Scholar

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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyAnhui UniversityHefeiChina
  2. 2.Institute of Automation, Chinese Academy of SciencesBeijingChina

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