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Level Set and Region Based Surface Propagation for Diffusion Tensor MRI Segmentation

  • Conference paper
Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis (MMBIA 2004, CVAMIA 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3117))

Abstract

Diffusion Tensor Imaging (DTI) is a relatively new modality for human brain imaging. During the last years, this modality has become widely used in medical studies. Tractography is currently the favorite technique to characterize and analyse the structure of the brain white matter. Only a few studies have been proposed to group data of particular interest. Rather than working on extracted fibers or on an estimated scalar value accounting for anisotropy as done in other approaches, we propose to extend classical segmentation techniques based on surface evolution by considering region statistics defined on the full diffusion tensor field itself. A multivariate Gaussian is used to approximate the density of the components of diffusion tensor for each sub-region of the volume. We validate our approach on synthetical data and we show promising results on the extraction of the corpus callosum from a real dataset.

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Author information

Authors and Affiliations

  1. I.N.R.I.A., B.P. 93, 2004 Route des Lucioles, 06902, Sophia Antipolis Cedex, France

    Mikaël Rousson, Christophe Lenglet & Rachid Deriche

Authors
  1. Mikaël Rousson
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  2. Christophe Lenglet
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  3. Rachid Deriche
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Iowa, 52242, Iowa City, IA, USA

    Milan Sonka

  2. Computational Biomedicine Lab, University of Houston, Houston, TX

    Ioannis A. Kakadiaris

  3. Center for Machine Perception, Czech Technical University, 121 35, Prague 2, Czech Republic

    Jan Kybic

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Rousson, M., Lenglet, C., Deriche, R. (2004). Level Set and Region Based Surface Propagation for Diffusion Tensor MRI Segmentation. In: Sonka, M., Kakadiaris, I.A., Kybic, J. (eds) Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis. MMBIA CVAMIA 2004 2004. Lecture Notes in Computer Science, vol 3117. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27816-0_11

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  • DOI: https://doi.org/10.1007/978-3-540-27816-0_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22675-8

  • Online ISBN: 978-3-540-27816-0

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