A Novel Approach for Image Alignment Using a Markov–Gibbs Appearance Model

  • Ayman El-Baz
  • Asem Ali
  • Aly A. Farag
  • Georgy Gimel’farb
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)


A new approach to align an image of a medical object with a given prototype is proposed. Visual appearance of the images, after equalizing their signals, is modelled with a new Markov-Gibbs random field with pairwise interaction model. Similarity to the prototype is measured by a Gibbs energy of signal co-occurrences in a characteristic subset of pixel pairs derived automatically from the prototype. An object is aligned by an affine transformation maximizing the similarity by using an automatic initialization followed by gradient search. Experiments confirm that our approach aligns complex objects better than popular conventional algorithms.


Image Registration Characteristic Neighbor Appearance Model Medical Object Pixel Pair 
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.


  1. 1.
    Maintz, J., Viergever, M.: A survey of medical image registration. Medical Image Analysis 2(1), 1–36 (1998)CrossRefGoogle Scholar
  2. 2.
    Zitova, B., Flusser, J.: Image registration methods: a survey. Image and Vision Computing 21, 977–1000 (2003)CrossRefGoogle Scholar
  3. 3.
    Pope, Theiler, J.: Automated image registration (AIR) of MTI imagery. Proc. SPIE 5093 27, 294–300 (2003)Google Scholar
  4. 4.
    Viola, P.: Alignment by maximization of mutual information. Ph.D. dissertation, MIT, Cambridge, MA (1995)Google Scholar
  5. 5.
    Pluim, J., Maintz, J., Viergever, M.: Mutual-information based registration of medical images: a survey. IEEE Trans. Medical Imaging 22(8) (August 2003)Google Scholar
  6. 6.
    Farag, A.A., El-Baz, A., Gimel’farb, G.: Precise Segmentation of Multi-modal Images. IEEE Transactions on Image Processing 15(4), 952–968 (2006)CrossRefGoogle Scholar
  7. 7.
    Studholme, C., et al.: An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition 32, 71–86 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ayman El-Baz
    • 1
  • Asem Ali
    • 1
  • Aly A. Farag
    • 1
  • Georgy Gimel’farb
    • 2
  1. 1.Computer Vision and Image Processing LaboratoryUniversity of LouisvilleLouisville
  2. 2.Department of Computer ScienceUniversity of AucklandAucklandNew Zealand

Personalised recommendations