Skip to main content
SpringerLink
Log in

A Systematic Literature Review on Multi-modal Medical Image Registration

  • Conference paper
  • First Online:
Service-Oriented Computing – ICSOC 2022 Workshops (ICSOC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13821))

Included in the following conference series:

  • 832 Accesses

Abstract

Context: In today’s health care, multi-modal image registration increasingly important role in medical analysis and diagnostics. Multi-modal image registration is a challenging task because of the different imaging conditions that changes from one imaging modality to another.

Objective: The purpose of this work is to determine the current state of the art in the field of medical image registration shedding light on techniques that have been used to register medical image combinations from different modalities and the importance of combining different modalities in automatic way in the medical domain.

Method: To fulfill this objective we chose a Systematic Literature Review (SLR) as method to follow. Which allows to collect and structure the information that exists in the field of multi-modal image registration.

Results: Several automatic solutions based on different registration techniques were proposed according to each specific modality combination.

Conclusion: The results provide the following conclusions: First, the machine learning in the recent years plays an important role in the automatic registration process. An important number of research propose a learning-based registration solution. Second, There few solutions in literature that tackle the automatic registration of histology - CT modality combination. Finally, the existing research work propose registration solutions for only combination of two modalities. A very few number of work suggest a tri-modality combining.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://xamflow.lucid.ch/.

References

  1. Alam, F., Rahman, S.U., Din, A.U., Qayum, F.: Medical image registration: classification, applications and issues. JPMI 32(4), 300 (2018)

    Google Scholar 

  2. Albers, J., et al.: Elastic transformation of histological slices allows precise co-registration with microCT data sets for a refined virtual histology approach. Sci. Rep. 11, 10846 (2021)

    Article  Google Scholar 

  3. Arimura, H.: Image-Based Computer-Assisted Radiation Therapy. Springer, Cham (2017)

    Book  Google Scholar 

  4. Bashiri, F.S., Baghaie, A., Rostami, R., Yu, Z., D’Souza, R.M.: Multi-modal medical image registration with full or partial data: a manifold learning approach. J. Imaging 5(1), 5 (2018)

    Article  Google Scholar 

  5. Cao, X., Yang, J., Wang, L., Xue, Z., Wang, Q., Shen, D.: Deep learning based inter-modality image registration supervised by intra-modality similarity. In: Shi, Y., Suk, H.-I., Liu, M. (eds.) MLMI 2018. LNCS, vol. 11046, pp. 55–63. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00919-9_7

    Chapter  Google Scholar 

  6. Cheng, X., Zhang, L., Zheng, Y.: Deep similarity learning for multimodal medical images. Comput. Methods Biomech. Biomed. Eng.: Imaging Vis. 6(3), 248–252 (2018)

    Google Scholar 

  7. El-Gamal, F.E.Z.A., Elmogy, M., Atwan, A.: Current trends in medical image registration and fusion. Egypt. Inf. J. 17(1), 99–124 (2016)

    Google Scholar 

  8. Ferrante, E., Oktay, O., Glocker, B., Milone, D.H.: On the adaptability of unsupervised CNN-based deformable image registration to unseen image domains. In: Shi, Y., Suk, H.-I., Liu, M. (eds.) MLMI 2018. LNCS, vol. 11046, pp. 294–302. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00919-9_34

    Chapter  Google Scholar 

  9. Hill, D.L., Batchelor, P.G., Holden, M., Hawkes, D.J.: Medical image registration. Phys. Med. Biol. 46(3), R1 (2001)

    Article  Google Scholar 

  10. Kitchenham, B.: Procedures for performing systematic reviews. Keele UK Keele Univ. 33(2004), 1–26 (2004)

    Google Scholar 

  11. Liao, R., Miao, S., de Tournemire, P., Grbic, S., Kamen, A., Mansi, T., Comaniciu, D.: An artificial agent for robust image registration. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, AAAI 2017, pp. 4168–4175. AAAI Press (2017)

    Google Scholar 

  12. Liu, B., Gao, X., Liu, H., Wang, X., Liang, B.: A fast weighted registration method of 3d point cloud based on curvature feature, pp. 83–87. ICMIP 2018, Association for Computing Machinery, Guiyang, China (2018)

    Google Scholar 

  13. Liu, Q., Leung, H.: Tensor-based descriptor for image registration via unsupervised network. In: 2017 20th International Conference on Information Fusion (Fusion), pp. 1–7. IEEE (2017)

    Google Scholar 

  14. Ma, K., et al.: Multimodal Image Registration with Deep Context Reinforcement Learning. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 240–248. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66182-7_28

    Chapter  Google Scholar 

  15. Maintz, J.A., Viergever, M.A.: A survey of medical image registration. Med. Image Anal. 2(1), 1–36 (1998)

    Article  Google Scholar 

  16. Miao, S., et al.: Dilated FCN for multi-agent 2d/3d medical image registration. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)

    Google Scholar 

  17. Rueckert, D., Schnabel, J.A.: Medical image registration. In: Deserno, T. (eds) Biomedical image processing, pp. 131–154. Springer, Berlin (2010). https://doi.org/10.1007/978-3-642-15816-2_5

  18. Sabokrohiyeh, S., Ang, K., Elbaz, M., Samavati, F.: Sketch-based registration of 3D cine MRI to 4D flow MRI. In: Proceedings of the Association for Computing Machinery, Stockholm, Sweden, ICBBT 2019, pp. 14–21 (2019)

    Google Scholar 

  19. Stergios, C., et al.: Linear and deformable image registration with 3D convolutional neural networks. In: Stoyanov, D., et al. (eds.) RAMBO/BIA/TIA -2018. LNCS, vol. 11040, pp. 13–22. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00946-5_2

    Chapter  Google Scholar 

  20. Tao, H., Lu, X.: A new 3d multi-modality medical bone image registration algorithm. In: Proceedings of the Association for Computing Machinery, Singapore, ICVIP 2017, pp. 140–145 (2017)

    Google Scholar 

  21. Uzunova, H., Wilms, M., Handels, H., Ehrhardt, J.: Training CNNs for image registration from few samples with model-based data augmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 223–231. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66182-7_26

    Chapter  Google Scholar 

  22. Valsecchi, A., Damas, S., Santamaria, J., Marrakchi-Kacem, L.: Genetic algorithms for voxel-based medical image registration. In: 2013 Fourth International Workshop on Computational Intelligence in Medical Imaging (CIMI), pp. 22–29. IEEE (2013)

    Google Scholar 

  23. Zheng, L., Qian, G.: A sift-based approach for image registration. In: Yang, Y., Ma, M. (eds.) Green Communications and Networks, vol. 113, pp. 277–287. Springer, Dordrecht (2012)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Kiel, Kiel, Germany

    Marwa Chaabane

  2. Scanco Medical AG, 8306, Brüttisellen, Switzerland

    Marwa Chaabane & Bruno Koller

Authors
  1. Marwa Chaabane
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bruno Koller
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marwa Chaabane .

Editor information

Editors and Affiliations

  1. University of Malaga, Málaga, Spain

    Javier Troya

  2. Politecnico di Milano, Milano, Italy

    Raffaela Mirandola

  3. University of Castilla-La Mancha, Albacete, Spain

    Elena Navarro

  4. University of the Republic, Montevideo, Uruguay

    Andrea Delgado

  5. University of Seville, Sevilla, Spain

    Sergio Segura

  6. University of Cádiz, Cádiz, Spain

    Guadalupe Ortiz

  7. Faculty of Informatics, Universita della Svizzera Italiana, Lugano, Switzerland

    Cesare Pautasso

  8. Karlsuhe Institute of Technology, Karlsruhe, Germany

    Christian Zirpins

  9. University of Seville, Seville, Spain

    Pablo Fernández

  10. ISA, Universidad de Sevilla, Sevilla, Spain

    Antonio Ruiz-Cortés

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chaabane, M., Koller, B. (2023). A Systematic Literature Review on Multi-modal Medical Image Registration. In: Troya, J., et al. Service-Oriented Computing – ICSOC 2022 Workshops. ICSOC 2022. Lecture Notes in Computer Science, vol 13821. Springer, Cham. https://doi.org/10.1007/978-3-031-26507-5_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-26507-5_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26506-8

  • Online ISBN: 978-3-031-26507-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation