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Large Deformation Image Classification Using Generalized Locality-Constrained Linear Coding*
Pei Zhang1, Chong-Yaw Wee1, Marc Niethammer2, Dinggang Shen1, and Pew-Thian Yap1
1Department of Radiology, The University of North Carolina at Chapel Hill, USA
peizhang@email.unc.edu
chongyaw_wee@med.unc.edu
dgshen@med.unc.edu
ptyap@med.unc.edu
2Department of Computer Science, Biomedical Research Imaging Center (BRIC), The University of North Carolina at Chapel Hill, USA
mn@cs.unc.edu
Abstract. Magnetic resonance (MR) imaging has been demonstrated to be very useful for clinical diagnosis of Alzheimer’s disease (AD). A common approach to using MR images for AD detection is to spatially normalize the images by non-rigid image registration, and then perform statistical analysis on the resulting deformation fields. Due to the high nonlinearity of the deformation field, recent studies suggest to use initial momentum instead as it lies in a linear space and fully encodes the deformation field. In this paper we explore the use of initial momentum for image classification by focusing on the problem of AD detection. Experiments on the public ADNI dataset show that the initial momentum, together with a simple sparse coding technique—locality-constrained linear coding (LLC)—can achieve a classification accuracy that is comparable to or even better than the state of the art. We also show that the performance of LLC can be greatly improved by introducing proper weights to the codebook.
*Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.ucla.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at http://adni.loni.ucla.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
LNCS 8149, p. 292 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2013