Model-based segmentation of individual brain structures from MRI data

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Abstract
This paper proposes a methodology that enables an arbitrary 3-D MRI brain image-volume to be automatically segmented and classified into neuro-anatomical components using multiresolution registration and matching with a novel volumetric brain structure model (VBSM). This model contains both raster and geometric data. The raster component comprises the mean MRI volume after a set of individual volumes of normal volunteers have been transformed to a standardized brain-based coordinate space. The geometric data consists of polyhedral objects representing anatomically important structures such as cortical gyri and deep gray matter nuclei. The method consists of iteratively registering the data set to be segmented to the VBSM using deformations based on local image correlation. This segmentation process is performed hierarchically in scale-space. Each step in decreasing levels of scale refines the fit of the previous step and provides input to the next. Results from phantom and real MR data are presented.

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