title: Geodesic regression for image time-series.
abstract: Registration of image-time series has so far been accomplished (i) by concatenating registrations between image pairs, (ii) by solving a joint estimation problem resulting in piecewise geodesic paths between image pairs, (iii) by kernel based local averaging or (iv) by augmenting the joint estimation with additional temporal irregularity penalties. Here, we propose a generative model extending least squares linear regression to the space of images by using a second-order dynamic formulation for image registration. Unlike previous approaches, the formulation allows for a compact representation of an approximation to the full spatio-temporal trajectory through its initial values. The method also opens up possibilities to design image-based approximation algorithms. The resulting optimization problem is solved using an adjoint method.
authors: UNC Chapel Hill, USA.
authors: Marc
authors: M
authors: Niethammer
authors: Yang
authors: Y
authors: Huang
authors: François-Xavier
authors: FX
authors: Vialard
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000375:Aging; D000465:Algorithms; D001921:Brain / Q000473:pathology; D001931:Brain Mapping / Q000379:methods*; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016018:Least-Squares Analysis; D008875:Middle Aged; D015233:Models, Statistical; D008962:Models, Theoretical; D012044:Regression Analysis; D013997:Time Factors