▪ Abstract Image segmentation plays a crucial role in many medical-imaging applications, by
automating or facilitating the delineation of anatomical structures and other regions of …

We present a technique for automatically assigning a neuroanatomical label to each voxel in
an MRI volume based on probabilistic information automatically estimated from a manually …

The finite mixture (FM) model is the most commonly used model for statistical segmentation
of brain magnetic resonance (MR) images because of its simple mathematical form and the …

We present a technique for automatically assigning a neuroanatomical label to each
location on a cortical surface model based on probabilistic information estimated from a …

We present a set of techniques for embedding the physics of the imaging process that
generates a class of magnetic resonance images (MRIs) into a segmentation or registration …

Characterizing the performance of image segmentation approaches has been a persistent
challenge. Performance analysis is important since segmentation algorithms often have …

We describe a new magnetic resonance (MR) image analysis tool that produces cortical
surface representations with spherical topology from MR images of the human brain. The …

Describes a fully automated method for model-based tissue classification of magnetic
resonance (MR) images of the brain. The method interleaves classification with estimation of …

The principal goal of the segmentation process is to partition an image into regions (also
called classes, or subsets) that are homogeneous with respect to one or more characteristics …

The partial volume effect (PVE) arises in volumetric images when more than one tissue type
occurs in a voxel. In such cases, the voxel intensity depends not only on the imaging …