Splines for Diffeomorphic Image Regression

Abstract

This paper develops a method for splines on diffeomorphisms for image regression. In contrast to previously proposed methods to capture image changes over time, such as geodesic regression, the method can capture more complex spatio-temporal deformations. In particular, it is a first step towards capturing periodic motions for example of the heart or the lung. Starting from a variational formulation of splines the proposed approach allows for the use of temporal control points to control spline behavior. This necessitates the development of a shooting formulation for splines. Experimental results are shown for synthetic and real data. The performance of the method is compared to geodesic regression.

Publication
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2014 - 17th International Conference, Boston, MA, USA, September 14-18, 2014, Proceedings, Part II
Nikhil Singh
Nikhil Singh
Postdoc
Marc Niethammer
Marc Niethammer
Professor of Computer Science

My research interests include image registration, image segmentation, shape analysis, machine learning, and biomedical applications.

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