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In this paper we present a novel method for three-dimensional segmentation and measurement of volumetric data based on the combi- nation of statistical and geometrical information. We represent the shape of complex three-dimensional structures, such as the cortex by combi- ning a discrete 3D simplex mesh with the construction of a smooth sur- face using triangular Gregory-Bezier patches. A Gaussian model for the tissues present in the image is adopted, and a classication procedure which also estimates and corrects for the bias eld present in the MRI is used. Condence bounds are produced for all the measurements, thus obtaining bounds on the position of the surface segmenting the image. Performance is illustrated on multiple sclerosis phantom data and on real data

Measurement of brain structures based on statistical and geometrical 3D segmentation