Skip to main content
SpringerLink
Log in
Book cover

New Trends in Networking, Computing, E-learning, Systems Sciences, and Engineering pp 117–124Cite as

Evaluation of Optimal Control-Based Deformable Registration Model

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 312))

Abstract

This paper presents an evaluation of an optimal control-based deformable image registration model and compares it to four well-known variational-based models, namely, elastic, fluid, diffusion and curvature models. Using similarity and deformation quality measures as performance indices, Non-dominated Sorting Genetic Algorithm (NSGA-II) is applied to approximate Pareto Fronts for each model to facilitate proper evaluation. The Pareto Fronts are also visualized using Level diagrams.

This is a preview of subscription content, 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   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
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

Learn about institutional subscriptions

References

  1. Mehmet Ali Akinlar. A New Method for Nonrigid Registration of 3D Images. PhD thesis, The University of Texas at Arlington, 2009.

    Google Scholar 

  2. Bernd Fischer and Jan Modersitzki. A unified approach to fast image registration and a new curvature based registration technique. 380:107–124, 15 March 2004.

    Google Scholar 

  3. Chih-Yao Hsieh. Nonrigid Image Registration by The Deformation Based Grid Generation. PhD thesis, The University of Texas at Arlington, 2008.

    Google Scholar 

  4. Eunjung Lee and Max Gunzburger. An Optimal Control Formulation of an Image Registration Problem. J. Math. Imaging Vis., 36(1):69–80, January 2010.

    Google Scholar 

  5. Stephen Taiwo Salako. Optimal Control Approach to Image Registration. PhD thesis, The University of Texas at Arlington, 2009.

    Google Scholar 

  6. Stuart Alexander MacGillivray. Curvature-based Image Registration: Review and Extensions. Technical report, University of Waterloo, 2009.

    Google Scholar 

  7. Margaret H. Wright Jeffrey C. Lagarias, James A Reeds and Paul E. Wright. Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions. SIAM J. OPTIM, 9(1):112–147, 1998.

    Article  MATH  MathSciNet  Google Scholar 

  8. Noppadol Chumchob and Ke Chen. A Robust Affine Image Registration Method. International Journal of Numerical Analysis and Modeling, 6(2):311–334, 2009.

    MATH  MathSciNet  Google Scholar 

  9. Mark Everingham and Henk Mullerand Barry Thomas. Evaluating Image Segmentation Algorithms Using the Pareto Front. In Proc. Seventh European Conf. Computer Vision, pages 34–48, 2002.

    Google Scholar 

  10. Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and T. Meyarivan. A Fast and Elitist Multi-Objective Genetic Algorithm: NSGA-II, 2000.

    Google Scholar 

  11. Aravind Sechadri. A Fast Elitist Multi-Objective Genetic Algorithm: NSGA-II. Documentation for Matlab Code Implementation, 2009.

    Google Scholar 

  12. Patrick M. Knupp. Hexahedral mesh untangling algebraic mesh quality metrics. In 9th International Meshing Roundtable, 2000.

    Google Scholar 

  13. Patrick M. Knupp. Algebraic Mesh Quality Metrics. SIAM J. Sci. Comput. 23(1):193–218, 2001.

    Article  MATH  MathSciNet  Google Scholar 

  14. Patrick M. Knupp. Algebraic Mesh Quality Metrics for Unstructured Initial Meshes. Finite Elem. Anal. Des., 39(3):217–241, January 2003.

    Google Scholar 

  15. E. Zio and R. Bazzo. Level Diagrams analysis of Pareto Front for multiobjective system redundancy allocation. Reliability Engineering & System Safety, 96(5):569–580, 2011.

    Article  Google Scholar 

  16. X. Blasco, J.M. Herrero, J. Sanchis, and M. Martinez. A new graphical visualization of n-dimensional Pareto front for decision-making in multiobjective optimization. Information Sciences, 178(20):3908–3924, 2008. Special Issue on Industrial Applications of Neural Networks 10th Engineering Applications of Neural Networks 2007.

    Google Scholar 

Download references

Acknowledgment

This work was supported by iThemba LABS, Medical Radiation Group through provision of the CT data set used to facilitate the evaluation procedure. Prof. Braae’s support and comments on this paper are highly appreciated.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, University of Cape Town, Cape Town, South Africa

    Naleli Jubert Matjelo & Fred Nicolls

  2. iThemba Laboratory for Accelerator Based Science, National Research Foundation, 722, Somerset West, 7129, South Africa

    Neil Muller

Authors
  1. Naleli Jubert Matjelo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fred Nicolls
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Neil Muller
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Naleli Jubert Matjelo .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, University of Bridgeport Associate Dean for Graduate Programs, Bridgeport, Connecticut, USA

    Khaled Elleithy

  2. Engineering and Computer Science, University of Bridgeport Dean of the School of Engineering, Bridgeport, Connecticut, USA

    Tarek Sobh

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Matjelo, N.J., Nicolls, F., Muller, N. (2015). Evaluation of Optimal Control-Based Deformable Registration Model. In: Elleithy, K., Sobh, T. (eds) New Trends in Networking, Computing, E-learning, Systems Sciences, and Engineering. Lecture Notes in Electrical Engineering, vol 312. Springer, Cham. https://doi.org/10.1007/978-3-319-06764-3_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-06764-3_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06763-6

  • Online ISBN: 978-3-319-06764-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation