Skip to main content

Advertisement

SpringerLink
Log in

An atlas-based multimodal registration method for 2D images with discrepancy structures

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

An atlas-based multimodal registration method for 2-dimension images with discrepancy structures was proposed in this paper. Atlas was utilized for complementing the discrepancy structure information in multimodal medical images. The scheme includes three steps: floating image to atlas registration, atlas to reference image registration, and field-based deformation. To evaluate the performance, a frame model, a brain model, and clinical images were employed in registration experiments. We measured the registration performance by the squared sum of intensity differences. Results indicate that this method is robust and performs better than the direct registration for multimodal images with discrepancy structures. We conclude that the proposed method is suitable for multimodal images with discrepancy structures.

An Atlas-based multimodal registration method schematic diagram

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Brainweb: Simulated brain database. http://brainweb.bic.mni.mcgill.ca/brainweb/

  2. Mirt: Medical image registration toolbox. https://sites.google.com/site/myronenko/research/mirt

  3. Alvén J, Norlén A, Enqvist O, Kahl F (2015) ÜBeratlas: robust speed-up of feature-based registration and multi-atlas segmentation. In: Scandinavian conference on image analysis. Springer, Berlin, pp 92–102

    Chapter  Google Scholar 

  4. Arabi H, Zaidi H (2016) One registration multi-atlas-based pseudo-CT generation for attenuation correction in PET/MRI. Eur J Nucl Med Mol Imaging 43(11):2021–2035

    Article  CAS  PubMed  Google Scholar 

  5. Ashburner J, Friston KJ (2005) Unified segmentation. Neuroimage 26(3):839–851

    Article  PubMed  Google Scholar 

  6. Avants BB, Tustison NJ, Stauffer M, Song G, Wu B, Gee JC (2014) The insight toolkit image registration framework. Front Neuroinform 8:44

    Article  PubMed  PubMed Central  Google Scholar 

  7. Bai J, Trinh TLH, Chuang KH, Qiu A (2012) Atlas-based automatic mouse brain image segmentation revisited: model complexity vs. image registration. Magn Reson Imaging 30(6):789–798

    Article  PubMed  Google Scholar 

  8. Bai W, Shi W, O’Regan DP, Tong T, Wang H, Jamil-Copley S, Peters NS, Rueckert D (2013) A probabilistic patch-based label fusion model for multi-atlas segmentation with registration refinement: application to cardiac mr images. IEEE Trans sMed Imaging 32(7):1302–1315

    Article  Google Scholar 

  9. Heinrich MP, Jenkinson M, Bhushan M, Matin T, Gleeson FV, Brady M, Schnabel JA (2012) Mind: modality independent neighbourhood descriptor for multi-modal deformable registration. Med Image Anal 16 (7):1423–1435

    Article  PubMed  Google Scholar 

  10. Kahl F, Alvén J, Enqvist O, Fejne F, Ulén J, Fredriksson J, Landgren M, Larsson V (2015) Good features for reliable registration in multi-atlas segmentation. Proceedings of the VISCERAL Challenge at ISBI 1390:12–17

    Google Scholar 

  11. Kallenberg M, Karssemeijer N (2012) Segmentation of the pectoral muscle in breast mri using atlas-based approaches. In: International conference on medical image computing and computer-assisted intervention, pp 371–8

  12. Legg PA, Rosin PL, Marshall D, Morgan JE (2015) Feature neighbourhood mutual information for multi-modal image registration: an application to eye fundus imaging. Pattern Recogn 48(6):1937–1946

    Article  Google Scholar 

  13. Lorenzo-Valdés M, Sanchez-Ortiz GI, Mohiaddin R, Rueckert D (2002) Atlas-based segmentation and tracking of 3d cardiac MR images using non-rigid registration. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 642–650

    Chapter  Google Scholar 

  14. Luan H, Qi F, Xue Z, Chen L, Shen D (2008) Multimodality image registration by maximization of quantitative–qualitative measure of mutual information. Pattern Recogn 41(1):285–298

    Article  Google Scholar 

  15. Mattes D, Haynor DR, Vesselle H, Lewellen TK, Eubank W (2003) PET-CT image registration in the chest using free-form deformations. IEEE Trans Med Imaging 22(1):120–128

    Article  PubMed  Google Scholar 

  16. Oktay O, Gomez A, Keraudren K, Schuh A, Bai W, Shi W, Penney G, Rueckert D (2015) Probabilistic edge map (PEM) for 3D ultrasound image registration and multi-atlas left ventricle segmentation. In: International conference on functional imaging and modeling of the heart. Springer, Berlin, pp 223–230

    Chapter  Google Scholar 

  17. Rivaz H, Collins DL (2012) Self-similarity weighted mutual information: a new nonrigid image registration metric. In: International conference on medical image computing and computer-assisted intervention, pp 91–8

    Chapter  Google Scholar 

  18. Rubeaux M, Nunes JC, Albera L, Garreau M (2014) Medical image registration using Edgeworth-based approximation of mutual information. Irbm 35(3):139–148

    Article  Google Scholar 

  19. Rueckert D, Sonoda LI, Hayes C, Hill DL, Leach MO, Hawkes DJ (1999) Nonrigid registration using free-form deformations: application to breast mr images. IEEE Trans Med Imaging 18(8):712–721

    Article  CAS  PubMed  Google Scholar 

  20. Studholme C, Hill DL, Hawkes DJ (1999) An overlap invariant entropy measure of 3D medical image alignment. Pattern Recogn 32(1):71–86

    Article  Google Scholar 

  21. Viola P, Wells III WM (1997) Alignment by maximization of mutual information. Int J Comput Vis 24(2):137–154

    Article  Google Scholar 

  22. Wang F, Vemuri BC (2007) Non-rigid multi-modal image registration using cross-cumulative residual entropy. Int J Comput Vis 74(2):201–215

    Article  PubMed  PubMed Central  Google Scholar 

  23. Wang F, Vemuri BC, Rao M, Chen Y (2003) Cumulative residual entropy, a new measure of information & its application to image alignment. In: Proceedings. Ninth IEEE international conference on Computer vision, 2003, IEEE, pp 548–553

  24. Yang X, Wu N, Cheng G, Zhou Z, David SY, Beitler JJ, Curran WJ, Liu T (2014) Automated segmentation of the parotid gland based on atlas registration and machine learning: a longitudinal mri study in head-and-neck radiation therapy. Int J Radiat Oncol Biol Phys 90(5):1225–1233

    Article  PubMed  PubMed Central  Google Scholar 

  25. Zhuang X, Shen J (2016) Multi-scale patch and multi-modality atlases for whole heart segmentation of MRI. Med Image Anal 31:77

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to thank L. Cao, J. Jv, and C. Xin for assisting in sorting data, and the anonymous reviewers for their valuable comments.

Funding

This study was supported in part by the National Natural Science Foundation of China (Grant nos. 61571036, 61502025, 61771039) and Beijing Municipal Science and Technology Commission Major Program (SX2016-04).

Author information

Authors and Affiliations

  1. Beijing Jiaotong Unversity, Beijing, China

    Wenchao Lv, Houjin Chen, Yahui Peng, Yanfeng Li & Jupeng Li

Authors
  1. Wenchao Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Houjin Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yahui Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yanfeng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jupeng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Houjin Chen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lv, W., Chen, H., Peng, Y. et al. An atlas-based multimodal registration method for 2D images with discrepancy structures. Med Biol Eng Comput 56, 2151–2161 (2018). https://doi.org/10.1007/s11517-018-1808-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-018-1808-1

Keywords

Search

Navigation