14 February 2012 Nonrigid free-form registration using landmark-based statistical deformation models
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Proceedings Volume 8314, Medical Imaging 2012: Image Processing; 831418 (2012); doi: 10.1117/12.911441
Event: SPIE Medical Imaging, 2012, San Diego, California, United States
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.
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Stefan Pszczolkowski, Luis Pizarro, Ricardo Guerrero, Daniel Rueckert, "Nonrigid free-form registration using landmark-based statistical deformation models", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 831418 (14 February 2012); doi: 10.1117/12.911441; http://dx.doi.org/10.1117/12.911441
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KEYWORDS
Image registration

Statistical modeling

Principal component analysis

Image processing

Medical imaging

Error analysis

Neuroimaging

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