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Robust Image Registration Based on Learning Prior Appearance Model

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Abstract

In this chapter, a novel approach to align an image of a textured object with a given prototype will be proposed. Visual appearance of the images, after equalizing their signals, is modeled with a Markov–Gibbs random field (MGRF) with pairwise interaction. 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 the proposed approach aligns complex objects better than the conventional algorithms used in alignment.

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Author information

Authors and Affiliations

  1. BioImaging Laboratory, Department of Bioengineering, University of Louisville, 423 Lutz Hall Building, Louisville, KY, USA

    Ayman El-Baz

  2. Department of Computer Science, The University of Auckland, Private Bag 92019, Auckland, 1149, New Zealand

    Georgy Gimel’farb

Authors
  1. Ayman El-Baz
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  2. Georgy Gimel’farb
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Corresponding author

Correspondence to Ayman El-Baz .

Editor information

Editors and Affiliations

  1. , Department of Bioengineering, University of Louisville, Louisville, 40208, USA

    Ayman S. El-Baz

  2. School of Engineering, Ngee Ann Polytechnic, Clementi Road 535, Singapore, 599489, Singapore

    Rajendra Acharya U

  3. , Department of Biomedical Engineering, Columbia University, New York, 10027, New York, USA

    Andrew F. Laine

  4. Biomedical Technologies, Inc., Denver, Colorado, USA

    Jasjit S. Suri

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© 2011 Springer Science+Business Media, LLC

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El-Baz, A., Gimel’farb, G. (2011). Robust Image Registration Based on Learning Prior Appearance Model. In: El-Baz, A., Acharya U, R., Laine, A., Suri, J. (eds) Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-8204-9_10

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