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Proceedings Paper

Non-rigid brain image registration using a statistical deformation model
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Paper Abstract

In this article, we propose a new registration method, based on a statistical analysis of deformation fields. At first, a set of MRI brain images was registered using a viscous fluid algorithm. The obtained deformation fields are then used to calculate a Principal Component Analysis (PCA) based decomposition. Since PCA models the deformations as a linear combination of statistically uncorrelated principal components, new deformations can be created by changing the coefficients in the linear combination. We then use the PCA representation of the deformation fields to non-rigidly align new sets of images. We use a gradient descent method to adjust the coefficients of the principal components, such that the resulting deformation maximizes the mutual information between the deformed image and an atlas image. The results of our method are promising. Viscous fluid registrations of new images can be recovered with an accuracy of about half a voxel. Better results can be obtained by using a more extensive database of learning images (we only used 84). Also, the optimization method used here can be improved, especially to shorten computation time.

Paper Details

Date Published: 10 March 2006
PDF: 8 pages
Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 614411 (10 March 2006); doi: 10.1117/12.653081
Show Author Affiliations
Jeroen Wouters, Katholieke Univ. Leuven, Univ. Hospital Gasthuisberg (Belgium)
Emiliano D'Agostino, Katholieke Univ. Leuven, Univ. Hospital Gasthuisberg (Belgium)
Frederik Maes, Katholieke Univ. Leuven, Univ. Hospital Gasthuisberg (Belgium)
Dirk Vandermeulen, Katholieke Univ. Leuven, Univ. Hospital Gasthuisberg (Belgium)
Paul Suetens, Katholieke Univ. Leuven, Univ. Hospital Gasthuisberg (Belgium)

Published in SPIE Proceedings Vol. 6144:
Medical Imaging 2006: Image Processing
Joseph M. Reinhardt; Josien P. W. Pluim, Editor(s)

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