title: Conformal invariants for multiply connected surfaces: Application to landmark curve-based brain morphometry analysis.
abstract: Landmark curves were widely adopted in neuroimaging research for surface correspondence computation and quantified morphometry analysis. However, most of the landmark based morphometry studies only focused on landmark curve shape difference. Here we propose to compute a set of conformal invariant-based shape indices, which are associated with the landmark curve induced boundary lengths in the hyperbolic parameter domain. Such shape indices may be used to identify which surfaces are conformally equivalent and further quantitatively measure surface deformation. With the surface Ricci flow method, we can conformally map a multiply connected surface to the Poincaré disk. Our algorithm provides a stable method to compute the shape index values in the 2D (Poincaré Disk) parameter domain. The proposed shape indices are succinct, intrinsic and informative. Experimental results with synthetic data and 3D MRI data demonstrate that our method is invariant under isometric transformations and able to detect brain surface abnormalities. We also applied the new shape indices to analyze brain morphometry abnormalities associated with Alzheimer' s disease (AD). We studied the baseline MRI scans of a set of healthy control and AD patients from the Alzheimer' s Disease Neuroimaging Initiative (ADNI: 30 healthy control subjects vs. 30 AD patients). Although the lengths of the landmarks in Euclidean space, cortical surface area, and volume features did not differ between the two groups, our conformal invariant based shape indices revealed significant differences by Hotelling' s T2test. The novel conformal invariant shape indices may offer a new sensitive biomarker and enrich our brain imaging analysis toolset for studying diagnosis and prognosis of AD.
authors.affiliation: School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, 85287, P.O. Box 878809, USA.
authors.forename: Jie
authors.initials: J
authors.lastname: Shi
authors.affiliation: School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, 85287, P.O. Box 878809, USA.
authors.forename: Wen
authors.initials: W
authors.lastname: Zhang
authors.affiliation: School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, 85287, P.O. Box 878809, USA.
authors.forename: Miao
authors.initials: M
authors.lastname: Tang
authors.affiliation: Department of Neurology, Mayo Clinic Arizona, Scottsdale, AZ, USA.
authors.forename: Richard J
authors.initials: RJ
authors.lastname: Caselli
authors.affiliation: School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, 85287, P.O. Box 878809, USA. Electronic address: ylwang@asu.edu.
authors.forename: Yalin
authors.initials: Y
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000175:diagnosis / Q000000981:diagnostic imaging / Q000473:pathology; D001921:Brain / Q000000981:diagnostic imaging* / Q000473:pathology; D006801:Humans; D007089:Image Enhancement; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging; D010363:Pattern Recognition, Automated
keywords: Alzheimer’s disease; Brain landmark curves; Conformal invariant; Teichmüller shape space