Multi-modal image set registration and atlas formation

Peter Lorenzen, Marcel Prastawa, Brad Davis, Guido Gerig, Elizabeth Bullitt, Sarang Joshi

    Research output: Contribution to journalArticlepeer-review


    In this paper, we present a Bayesian framework for both generating inter-subject large deformation transformations between two multi-modal image sets of the brain and for forming multi-class brain atlases. In this framework, the estimated transformations are generated using maximal information about the underlying neuroanatomy present in each of the different modalities. This modality independent registration framework is achieved by jointly estimating the posterior probabilities associated with the multi-modal image sets and the high-dimensional registration transformations mapping these posteriors. To maximally use the information present in all the modalities for registration, Kullback-Leibler divergence between the estimated posteriors is minimized. Registration results for image sets composed of multi-modal MR images of healthy adult human brains are presented. Atlas formation results are presented for a population of five infant human brains.

    Original languageEnglish (US)
    Pages (from-to)440-451
    Number of pages12
    JournalMedical Image Analysis
    Issue number3 SPEC. ISS.
    StatePublished - Jun 2006


    • Atlas formation
    • Computational anatomy
    • Information theory
    • Inverse consistent registration
    • Medical image analysis
    • Multi-modal image set registration

    ASJC Scopus subject areas

    • Radiological and Ultrasound Technology
    • Radiology Nuclear Medicine and imaging
    • Computer Vision and Pattern Recognition
    • Health Informatics
    • Computer Graphics and Computer-Aided Design


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