Statistical Shape Analysis for Population Studies via Level-Set Based Shape Morphing

Abstract

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 by a novel level-set framework for shape morphing and a multi-shape dissimilarity-measure derived by 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 different populations using a variety of brain structures including left and right striatum, caudate, amygdala-hippocampal complex and superior- temporal gyrus (STG) in normal controls and patients. The synthetic databases allow quantitative evaluations of the proposed algorithm while the results obtained for the real clinical data are in line with published findings on gray matter reduction in the tested cortical and sub-cortical structures in schizophrenia patients.