The deformable atlas paradigm has been at the core of computational anatomy during the
last two decades. Spatial normalization is the variant endowing the atlas with a coordinate …

In non-rigid registration, the tradeoff between warp regularization and image fidelity is
typically determined empirically. In atlas-based segmentation, this leads to a probabilistic …

Atlas registration is a recognized paradigm for the automatic segmentation of normal MR
brain images. Unfortunately, atlas-based segmentation has been of limited use in presence …

Magnetic resonance imaging (MRI) is commonly employed for the depiction of soft tissues,
most notably the human brain. Computer-aided image analysis techniques lead to image …

In this paper, we propose a unified framework for computing atlases from manually labeled
data at various degrees of “sharpness” and the joint registration-segmentation of a new …

Quantifying the effect of a genetic manipulation or disease is a complicated process in a
population of animals. Probabilistic brain atlases can capture population variability and be …

Brain atlases have a wide range of use from education to research to clinical applications.
Mathematical methods as well as computational methods and tools play a major role in the …

Simulated deformations and images can act as the gold standard for evaluating various
template-based image segmentation and registration algorithms. Traditional deformable …

Large variations occur in brain anatomical structures in human populations, presenting a
critical challenge to the brain mapping process. This study investigates the major impact of …

Approaches to brain image spatial normalization conventionally make use of a target image
to which each subject image is separately matched. In many cases the use of a single brain …