Optimal Parameters Selection for Non-parametric Image Registration Methods

  • Jorge Larrey-Ruiz
  • Juan Morales-Sánchez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4179)


Choosing the adequate registration and simulation parameters in non-parametric image registration methods is an open question. There is no agreement about which are the optimal values (if any) for these parameters, since they depend on the images to be registered. As a result, in the literature the parameters involved in the registration process are arbitrarily fixed by the authors. The present paper is intended to address this issue. A two-step method is proposed to obtain the optimal values of these parameters, in terms of achieving in a minimum number of iterations the best trade-off between similarity of the images and smoothness of the transformation. These optimal values minimize the joint energy functional defined in a variational framework. We focus on the specific formulation of diffusion and curvature registration, but the exposed methodology can be directly applied to other non-parametric registration schemes. The proposed method is validated over different registration scenarios.


Regularization Parameter Image Registration Similarity Energy Registration Scheme Register Template 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jorge Larrey-Ruiz
    • 1
  • Juan Morales-Sánchez
    • 1
  1. 1.Grupo de Teoría y Tratamiento de la Senal, Departamento de las Tecnologías de la Información y las ComunicacionesUniversidad Politécnica de CartagenaCartagena (Murcia)Spain

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