Efficient population registration of 3d data

Authors:
Lilla Zöllei;Erik Learned-Miller;Eric Grimson;William Wells
Affiliations:
Computer Science and Artificial Intelligence Lab, MIT;Dept. of Computer Science, University of Massachusetts, Amherst;Computer Science and Artificial Intelligence Lab, MIT;Computer Science and Artificial Intelligence Lab, MIT
Venue:
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Year:
2005

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Abstract

We present a population registration framework that acts on large collections or populations of data volumes. The data alignment procedure runs in a simultaneous fashion, with every member of the population approaching the central tendency of the collection at the same time. Such a mechanism eliminates the need for selecting a particular reference frame a priori, resulting in a non-biased estimate of a digital atlas. Our algorithm adopts an affine congealing framework with an information theoretic objective function and is optimized via a gradient-based stochastic approximation process embedded in a multi-resolution setting. We present experimental results on both synthetic and real images.