Camille Izard

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In medical imaging, finding landmarks that provide biologically meaningful correspondences is often a challenging and time-consuming manual task. In this paper we propose a generic and simple algorithm for landmarking non-cortical brain structures automatically. We use a probabilistic model of the image intensities based on the deformation of a tissue(More)
We present a generic statistical deformable model for gray level medical images and propose to use it for template matching. Template matching methods usually rely on the arbitrary choice of a cost function and a template. Statistical models , on the other hand, allow us to derive optimal learning and matching algorithms from the modeling assumptions using(More)
In order to analyze Magnetic Resonance brain Images (MRI), neu-robiologists need to define landmarks. These points characterize the anatomy and are identifiable on each subject, so that they can be used as a reference system. Manually detecting these points in 3D structures is tricky even for experienced neuroscientists. This work focuses on the automatic(More)
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