Application of new totally structured secant method to registration of time series images

Shao Yan Sun, Huan Wen Tang, Yi Yuan Tang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Brain functional imaging is an important tool in the study of nerval science, and it is necessary to register time series images. The new totally structured secant method is adopted to optimize the nonlinear least squares registration criteria, which gets a better approximation to the Hessian of the criteria by secant update, while the conventional method - Gauss-Newton algorithm directly gets rid of the second-order information of the Hessian. At the same time, the update matrix is always symmetrical and positive which makes it reversible, so a descent direction can be found in each step. The simulation registration results show that the algorithm can quickly obtain the best registration parameters with high precision.

Original languageEnglish
Pages (from-to)301-304
Number of pages4
JournalDalian Ligong Daxue Xuebao/Journal of Dalian University of Technology
Volume47
Issue number2
StatePublished - Mar 2007

Keywords

  • BFGS-update
  • Criteria
  • FMRI
  • Image registration
  • Quasi-Newton equation
  • Structured secant method

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