title: Shape analysis with overcomplete spherical wavelets.
abstract: In this paper, we explore the use of over-complete spherical wavelets in shape analysis of closed 2D surfaces. Previous work has demonstrated, theoretically and practically, the advantages of overcomplete over bi-orthogonal spherical wavelets. Here we present a detailed formulation of over-complete wavelets, as well as shape analysis experiments of cortical folding development using them. Our experiments verify in a quantitative fashion existing qualitative theories of neuroanatomical development. Furthermore, the experiments reveal novel insights into neuro-anatomical development not previously documented.
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, MIT, USA. ythomas@csail.mit.edu
authors.forename: B T Thomas
authors.initials: BT
authors.lastname: Yeo
authors.forename: Peng
authors.initials: P
authors.lastname: Yu
authors.forename: P Ellen
authors.initials: PE
authors.lastname: Grant
authors.forename: Bruce
authors.initials: B
authors.lastname: Fischl
authors.forename: Polina
authors.initials: P
authors.lastname: Golland
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007231:Infant, Newborn; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity