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Proceedings Paper

Nonrigid free-form registration using landmark-based statistical deformation models
Author(s): Stefan Pszczolkowski; Luis Pizarro; Ricardo Guerrero; Daniel Rueckert
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Paper Abstract

In this paper, we propose an image registration algorithm named statistically-based FFD registration (SFFD). This registration method is a modification of a well-known free-form deformations (FFD) approach. Our framework dramatically reduces the number of parameters to optimise and only needs to perform a single-resolution optimisation to account for coarse and fine local displacements, in contrast to the multi-resolution strategy employed by the FFD-based registration. The proposed registration uses statistical deformation models (SDMs) as a priori knowledge to guide the alignment of a new subject to a common reference template. These SDMs account for the anatomical mean and variability across a population of subjects. We also propose that available anatomical landmark information can be encoded within the proposed SDM framework to enforce the alignment of certain anatomical structures. We present results in terms of fiducial localisation error, which illustrate the ability of the SDMs to encode landmark position information. We also show that our statistical registration algorithm can provide registration results comparable to the standard FFD-based approach at a much lower computational cost.

Paper Details

Date Published: 14 February 2012
PDF: 9 pages
Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 831418 (14 February 2012); doi: 10.1117/12.911441
Show Author Affiliations
Stefan Pszczolkowski, Imperial College London (United Kingdom)
Luis Pizarro, Imperial College London (United Kingdom)
Ricardo Guerrero, Imperial College London (United Kingdom)
Daniel Rueckert, Imperial College London (United Kingdom)

Published in SPIE Proceedings Vol. 8314:
Medical Imaging 2012: Image Processing
David R. Haynor; Sébastien Ourselin, Editor(s)

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