Comparative Evaluation of Regression Methods for 3D-2D Image Registration

  • Ana Isabel Rodrigues Gouveia
  • Coert Metz
  • Luís Freire
  • Stefan Klein
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7553)


We perform a comparative evaluation of different regression techniques for 3D-2D registration-by-regression. In registration-by-regression, image registration is treated as a nonlinear regression problem that relates image features of 2D projection images to the transformation parameters of the 3D image. In this work, we evaluate seven regression methods: Multiple Linear and Polynomial Regression (LR and PR), k-Nearest Neighbour (k-NN), Multiple Layer Perceptron with conjugate gradient optimization (MLP-CG) and with Levenberg-Marquardt optimization (MLP-LM), Radial Basis Function network (RBF) and Support Vector Regression (SVR). The experiments are performed using simulated X-ray images (DRRs) of nine coronary vessel trees, allowing us to compute the mean target registration error (mTRE) to the ground truth. All methods were robust to large initial misalignment and the highest accuracy was achieved using MLP-LM and RBF.


3D-2D image registration regression Multiple Layer Perceptron Radial Basis Function network Support Vector Regression coronary arteries 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ana Isabel Rodrigues Gouveia
    • 1
    • 2
  • Coert Metz
    • 3
  • Luís Freire
    • 4
  • Stefan Klein
    • 3
  1. 1.CICS-UBI – Health Sciences Research CentreUniversity of Beira InteriorCovilhãPortugal
  2. 2.Institute of Biophysics and Biomedical EngineeringUniversity of LisbonLisbonPortugal
  3. 3.Depts. of Medical Informatics & Radiology, Erasmus MCBiomedical Imaging Group RotterdamRotterdamThe Netherlands
  4. 4.Escola Superior de Tecnologia da Saúde de LisboaInstituto Politécnico de LisboaLisbonPortugal

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