title: Parameterization-invariant shape comparisons of anatomical surfaces.
abstract: We consider 3-D brain structures as continuous parameterized surfaces and present a metric for their comparisons that is invariant to the way they are parameterized. Past comparisons of such surfaces involve either volume deformations or nonrigid matching under fixed parameterizations of surfaces. We propose a new mathematical representation of surfaces, called q-maps, such that L² distances between such maps are invariant to re-parameterizations. This property allows for removing the parameterization variability by optimizing over the re-parameterization group, resulting in a proper parameterization-invariant distance between shapes of surfaces. We demonstrate this method in shape analysis of multiple brain structures, for 34 subjects in the Detroit Fetal Alcohol and Drug Exposure Cohort study, which results in a 91% classification rate for attention deficit hyperactivity disorder cases and controls. This method outperforms some existing techniques such as spherical harmonic point distribution model (SPHARM-PDM) or iterative closest point (ICP).
authors.affiliation: Department of Statistics, Florida State University, Tallahassee, FL 32306, USA. skurtek@stat.fsu.edu
authors.forename: Sebastian
authors.initials: S
authors.lastname: Kurtek
authors.forename: Eric
authors.initials: E
authors.lastname: Klassen
authors.forename: Zhaohua
authors.initials: Z
authors.lastname: Ding
authors.forename: Sandra W
authors.initials: SW
authors.lastname: Jacobson
authors.forename: Joseph L
authors.initials: JL
authors.lastname: Jacobson
authors.forename: Malcolm J
authors.initials: MJ
authors.lastname: Avison
authors.forename: Anuj
authors.initials: A
authors.lastname: Srivastava
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity