State-of-the-Art Medical Image Registration Methodologies: A Survey

  • Fahmi Khalifa
  • Garth M. Beache
  • Georgy Gimel’farb
  • Jasjit S. Suri
  • Ayman S. El-BazEmail author


Almost all computer vision applications, from remote sensing and cartography to medical imaging and biometrics, use image registration or alignment techniques that establish spatial correspondence (one-to-one mapping) between two or more images. These images depict either one planar (2-D) or volumetric (3-D) scene or several such scenes and can be taken at different times, from various viewpoints, and/or by multiple sensors. In medical image processing and analysis, the image registration is instrumental for clinical diagnosis and therapy planning, e.g., to follow disease progression and/or response to treatment, or integrate information from different sources/modalities to form more detailed descriptions of anatomical objects-of-interest. The unified registration goal – aligning a 2-D or 3-D target (sensed) image with a reference image – is reached by specifying a mathematical model of image transformations for and determining model parameters of the desired alignment. Frequently, the parameters provide an optimum of a goal function supported by the parameter space, so that the registration reduces to a certain optimization problem. This chapter overviews the 2-D and the 3-D medical image registration with special reference to the state-of-the-art robust techniques proposed for the last decade and discusses their advantages, drawbacks, and practical implementations.


Image registration Similarity functions Image transformations Global registration Nonrigid registration Numerical optimization Image resampling 


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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Fahmi Khalifa
  • Garth M. Beache
  • Georgy Gimel’farb
  • Jasjit S. Suri
  • Ayman S. El-Baz
    • 1
    Email author
  1. 1.BioImaging Laboratory, Department of BioengineeringUniversity of LouisvilleLouisvilleUSA

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