Title: Transform-invariant feature based functional MR image registration and neural activity modelling
Authors: Jiaqi Gong; Qi Hao; Fei Hu
Addresses: Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA ' Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA ' Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA
Abstract: In this paper, a set of non-rigid image registration and neural activity modelling methods using functional MR Images (fMRI) are proposed based on transform-invariant feature representations. Our work made two contributions. First, we propose to use a transform-invariant feature to improve image registration performance of Iterative Closest Point (ICP) based methods. The proposed feature utilises Gaussian Mixture Models (GMM) to describe the local topological structure of fMRI data. Second, we propose to use a 3-dimensional Scale-Invariant Feature Transform (SIFT) based descriptor to represent neural activities related to drinking behaviour. As a result, neural activities patterns of different subjects drinking water or intaking glucose can be recognised, with strong robustness against various artefacts.
Keywords: image registration; iterative closest point; local topological descriptor; Gaussian mixture models; scale-invariant feature transform; MRI; magnetic resonance imaging; neural activity modelling; functional MR images; drinking behaviour; drinking water; intaking glucose.
International Journal of Computational Biology and Drug Design, 2013 Vol.6 No.3, pp.175 - 189
Published online: 29 Jul 2013 *Full-text access for editors Access for subscribers Purchase this article Comment on this article