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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Geodesic Regression for Image Time-Series
verfasst von : Marc Niethammer, Yang Huang, François-Xavier Vialard
Erschienen in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011
Verlag: Springer Berlin Heidelberg
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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.