Statistical shape analysis of neuroanatomical structures via level-set-based shape morphing

Groupwise statistical analysis of the morphometry of brain structures plays an important role in neuroimaging studies. Nevertheless, most morphometric measurements are often limited to volume and surface area, as further morphological characterization of anatomical structures poses a significant challenge. In this paper, we present a method that allows the detection, localization, and quantification of statistically significant morphological differences in complex brain structures between populations. This is accomplished b y a novel level-set framework for shape morphing and a multishape dissimilarity-measure derived b y a modified version of the Hausdorff distance. The proposed method does not require explicit one-to-one point correspondences and is fast, robust, and easy to implement regardless of the topological complexity of the anatomical surface under study. The proposed model has been applied to well-defined regions of interest using both synthetic and real data sets. This includes the corpus callosum, striatum, caudate, amygdala-hippocampal complex, and superior temporal gyrus. These structures were selected for their importance with respect to brain regions implicated in a variety of neurological disorders. The synthetic databases allowed quantitative evaluations of the meth od. Results obtained with real clinical data of Williams syndrome and schizophrenia patients agree w ith published findings in the psychiatry literature.