title: Depth-based shape-analysis.
abstract: In this paper we propose a new method for shape analysis based on the depth-ordering of shapes. We use this depth-ordering to non-parametrically define depth with respect to a normal control population. This allows us to quantify differences with respect to "normality". We combine this approach with a permutation test allowing it to test for localized shape differences. The method is evaluated on a synthetically generated striatum dataset as well as on a real caudate dataset.
authors.forename: Yi
authors.initials: Y
authors.lastname: Hong
authors.forename: Yi
authors.initials: Y
authors.lastname: Gao
authors.forename: Marc
authors.initials: M
authors.lastname: Niethammer
authors.forename: Sylvain
authors.initials: S
authors.lastname: Bouix
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D004827:Epilepsy / Q000473:pathology*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012569:Schizotypal Personality Disorder / Q000473:pathology*; D012680:Sensitivity and Specificity