Skip to main content
SpringerLink
Log in

A SIFT-Based Approach for Image Registration

  • Conference paper
  • First Online:
Green Communications and Networks

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 113))

Abstract

Over the past several decades, image registration has emerged as one of the key technologies in medical image computing with applications ranging from computer assisted diagnosis to computer aided therapy and surgery. In this paper, we present a new method for medical image registration, which is based on the Scale-invariant feature transform (SIFT) and TPS. Our experimental results show that the proposed method could achieve greater competitive performance than TPS-based image registration technique.

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 429.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 549.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 549.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  1. Hajnal JV, Hawkes DJ, Hill DLG (2011) Medical image registration. CRC, London

    Google Scholar 

  2. Brown L (1992) A survey of image registration technique. ACM Comput Surv 24:325–376

    Article  Google Scholar 

  3. Zitová B, Flusser J (2003) Image registration methods: a survey. Image Vis Comput 21:977–1000

    Article  Google Scholar 

  4. Shams R, Sadeghi P, Kennedy R, Hartley R (2010) A survey of medical image registration on multicore and the GPU. Signal Process Mag IEEE 27(2):50–60

    Article  Google Scholar 

  5. Schaefer S, Mcphail T, Warren J (2006) Image deformation Using moving least squares. ACM Trans Graph 25(3):533–540 Jul

    Article  Google Scholar 

  6. Lowe DG (1999) Object recognition from local scale-invariant features. Proc Int Conf Comput Vis 2:1150–1157

    Google Scholar 

  7. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:91–110

    Article  Google Scholar 

  8. Koshani R, Hub M, Balter J et al (2009) Objective assessment of deformable image registration in radiotherapy: a multi-institution study. Med Phys 35(12):5944–5953

    Article  Google Scholar 

  9. Klein A, Andersson J, Ardekani BA et al (2009) Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage 46(3):786–802

    Article  Google Scholar 

  10. Dandekar O, Shekhar R (2007) FPGA-accelerated deformable image registration for improved target-delineation during CT-guided interventions. IEEE Trans Biomed Circuits Syst 1(2):116–127

    Google Scholar 

Download references

Acknowledgment

This chapter was supported by Zhejiang Provincial Natural Science Foundation of China (Grant No. Y1100018).

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Zhejiang University, Hangzhou, China

    Lintao Zheng & Guiping Qian

Authors
  1. Lintao Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guiping Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lintao Zheng .

Editor information

Editors and Affiliations

  1. , Department of Electronic Engineering, Shanghai Jiao Tong University, Dongchuan Road 800, Shanghai, 200240, China, People's Republic

    Yuhang Yang

  2. , Electrical & Electronic Engineering, Nanyang Technological University, Nanyang Avenue, Singapore, 639798, Singapore

    Maode Ma

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media B.V.

About this paper

Cite this paper

Zheng, L., Qian, G. (2012). A SIFT-Based Approach for Image Registration. In: Yang, Y., Ma, M. (eds) Green Communications and Networks. Lecture Notes in Electrical Engineering, vol 113. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-2169-2_34

Download citation

  • DOI: https://doi.org/10.1007/978-94-007-2169-2_34

  • Published:

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-007-2168-5

  • Online ISBN: 978-94-007-2169-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation