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Robust registration of dissimilar single and multimodal images

  • Christophoros Nikou
  • Fabrice Heitz
  • Jean -Paul Armspach
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1407)

Abstract

In this paper, we develop data driven registration algorithms, relying on robust pixel similarity metrics, that enable an accurate (sub-pixel) rigid registration of dissimilar single and multimodal 2D/3D images. A “soft redescending” estimator is associated to a top down stochatic multigrid relaxation algorithm in order to obtain robust, data driven multimodal image registrations. With the stochastic multigrid strategy, the registration is not affected by local minima in the objective function and a manual initialization near the optimal solution is not necessary. The proposed robust similarity metrics are compared to the most popular standard similarity metrics, on synthetic as well as on real world image pairs showing gross dissimilarities. Two case-studies are considered: the registration of single and multimodal 3D medical images and the matching of multispectral remotely sensed images showing large overcast areas.

Keywords

Mutual Information Image Registration Multimodal Image Registration Error Similarity Metrics 
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 1998

Authors and Affiliations

  • Christophoros Nikou
    • 1
    • 2
  • Fabrice Heitz
    • 1
  • Jean -Paul Armspach
    • 2
  1. 1.LSIIT UPRES-A CNRS 7005Université Strasbourg IIllkirchFrance
  2. 2.Institut de Physique Biologique UPRES-A CNRS 7004 Faculté de MédecineUniversité Strasbourg IStrasbourgFrance

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