Non-local Robust Detection of DTI White Matter Differences with Small Databases

  • Olivier Commowick
  • Aymeric Stamm
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7512)


Diffusion imaging, through the study of water diffusion, allows for the characterization of brain white matter, both at the population and individual level. In recent years, it has been employed to detect brain abnormalities in patients suffering from a disease, e.g. from multiple sclerosis (MS). State-of-the-art methods usually utilize a database of matched (age, sex, ...) controls, registered onto a template, to test for differences in the patient white matter. Such approaches however suffer from two main drawbacks. First, registration algorithms are prone to local errors, thereby degrading the comparison results. Second, the database needs to be large enough to obtain reliable results. However, in medical imaging, such large databases are hardly available. In this paper, we propose a new method that addresses these two issues. It relies on the search for samples in a local neighborhood of each pixel to increase the size of the database. Then, we propose a new test based on these samples to perform a voxelwise comparison of a patient image with respect to a population of controls. We demonstrate on simulated and real MS patient data how such a framework allows for an improved detection power and a better robustness and reproducibility, even with a small database.


Multiple Sclerosis Multiple Sclerosis Patient Registration Error Registration Algorithm Small Database 
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 2012

Authors and Affiliations

  • Olivier Commowick
    • 1
  • Aymeric Stamm
    • 1
  1. 1.VISAGES: INSERM U746 - CNRS UMR6074 - INRIA - Univ. of Rennes IFrance

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