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Piecewise Affine Registration of Biological Images

  • Alain Pitiot
  • Grégoire Malandain
  • Eric Bardinet
  • Paul M. Thompson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2717)

Abstract

his manuscript tackles the registration of 2D biological images (histological sections or autoradiographs) to 2D images from the same or different modalities (e.g., histology or MRI). The process of acquiring these images typically induces composite transformations that can be modeled as a number of rigid or affine local transformations embedded in an elastic one. We propose a registration approach closely derived from this model. Given a pair of input images, we first compute a dense similarity field between them with a block matching algorithm. A hierarchical clustering algorithm then automatically partitions this field into a number of classes from which we extract independent pairs of sub-images. Finally, the pairs of sub-images are, independently, affinely registered and a hybrid affine/non-linear interpolation scheme is used to compose the output registered image. We investigate the behavior of our approach under a variety of conditions, and discuss examples using real biomedical images, including MRI, histology and cryosection data.

Keywords

Input Image Reference Image Geodesic Distance Local Transformation Biological Image 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Alain Pitiot
    • 1
    • 2
  • Grégoire Malandain
    • 1
  • Eric Bardinet
    • 1
    • 3
  • Paul M. Thompson
    • 2
  1. 1.Epidaure, INRIASophia-AntipolisFrance
  2. 2.LONI, UCLA School of MedicineLos AngelesUSA
  3. 3.CNRS UPR640-LENAParisFrance

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