Data Fusion and Fuzzy Spatial Relationships for Locating Deep Brain Stimulation Targets in Magnetic Resonance Images

  • Alice Villéger
  • Lemlih Ouchchane
  • Jean-Jacques Lemaire
  • Jean-Yves Boire
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4179)


Symptoms of Parkinson’s disease can be relieved through Deep Brain Stimulation. This neurosurgical technique relies on high precision positioning of electrodes in specific areas of the basal ganglia and the thalamus. In order to identify these anatomical targets, which are located deep within the brain, we developed a semi-automated method of image analysis, based on data fusion. Information provided by both anatomical magnetic resonance images and expert knowledge is managed in a common possibilistic frame, using a fuzzy logic approach. More specifically, a graph-based virtual atlas modeling theoretical anatomical knowledge is matched to the image data from each patient, through a research algorithm (or strategy) which simultaneously computes an estimation of the location of every structures, thus assisting the neurosurgeon in defining the optimal target. The method was tested on 10 images, with promising results. Location and segmentation results were statistically assessed, opening perspectives for enhancements.


Deep Brain Stimulation Segmentation Result Data Fusion Subthalamic Nucleus Fusion Algorithm 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Alice Villéger
    • 1
  • Lemlih Ouchchane
    • 1
    • 2
  • Jean-Jacques Lemaire
    • 1
    • 3
  • Jean-Yves Boire
    • 1
    • 2
  1. 1.ERIM, Medicine Faculty of Clermont-FerrandAuvergne UniversityFrance
  2. 2.Biostatistics UnitUniversity Hospital of Clermont-FerrandFrance
  3. 3.Department of NeurosurgeryUniversity Hospital of Clermont-FerrandFrance

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