Abstract
A new technique for the automated measurement of a variety of gross neuroanatomical structures (such as cerebral ventricles) is described. The system exploits the local statistical properties of dual-echo magnetic resonance images of the brain to produce a fully unsupervised classification of these images into a variety of tissue types. Following this, a simple region agglomeration procedure aggregates the segmented regions by class, and submits these regions to a computational-geometric analysis from which a wide variety of measures are derived.
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