作者: Yalin Wang , Jie Shi , Xiaotian Yin , Xianfeng Gu , Tony F. Chan
关键词: Mathematical analysis 、 Mathematics 、 Ricci flow 、 Curvature 、 Surface (mathematics) 、 Boundary (topology) 、 Gaussian curvature 、 Geometry 、 Gaussian 、 Manifold 、 Conformal map
摘要: In brain mapping research, parameterized 3-D surface models are of great interest for statistical comparisons anatomy, surface-based registration, and signal processing. Here, we introduce the theories continuous discrete Ricci flow, which can create Riemannian metrics on surfaces with arbitrary topologies user-defined Gaussian curvatures. The resulting conformal parameterizations have no singularities they intrinsic stable. First, convert a cortical model into multiple boundary by cutting along selected anatomical landmark curves. Secondly, conformally parameterize each to parameter domain user-designed curvature arrangement. domain, shape index based invariants is computed, inter-subject matching performed solving constrained harmonic map. We illustrate various target arrangements demonstrate stability method using longitudinal data. To map differences in morphometry, studied asymmetry 14 healthy control subjects. used manifold version Hotelling's T2 test, applied Jacobian matrices parameterizations. A permutation cumulative distribution p-values, were estimate overall significance differences. results show our algorithm's power detect subtle group surfaces.