A SIFT-Based Approach for Image Registration

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


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.


SIFT MLS Non rigid registration Medical image 



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


  1. 1.
    Hajnal JV, Hawkes DJ, Hill DLG (2011) Medical image registration. CRC, LondonGoogle Scholar
  2. 2.
    Brown L (1992) A survey of image registration technique. ACM Comput Surv 24:325–376CrossRefGoogle Scholar
  3. 3.
    Zitová B, Flusser J (2003) Image registration methods: a survey. Image Vis Comput 21:977–1000CrossRefGoogle Scholar
  4. 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):50–60CrossRefGoogle Scholar
  5. 5.
    Schaefer S, Mcphail T, Warren J (2006) Image deformation Using moving least squares. ACM Trans Graph 25(3):533–540 JulCrossRefGoogle Scholar
  6. 6.
    Lowe DG (1999) Object recognition from local scale-invariant features. Proc Int Conf Comput Vis 2:1150–1157Google Scholar
  7. 7.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:91–110CrossRefGoogle Scholar
  8. 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):5944–5953CrossRefGoogle Scholar
  9. 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):786–802CrossRefGoogle Scholar
  10. 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):116–127Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringZhejiang UniversityHangzhouChina

Personalised recommendations