Large deformation image classification using generalized locality-constrained linear coding.

Pei Zhang, Chong Yaw Wee, Marc Niethammer, Dinggang Shen, Pew Thian Yap

Research output: Chapter in Book/Report/Conference proceedingChapter

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.

Original languageEnglish
Title of host publicationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages292-299
Number of pages8
Volume16
EditionPt 1
Publication statusPublished - 2013 Dec 1
Externally publishedYes

Fingerprint

Alzheimer Disease
Magnetic Resonance Spectroscopy
Magnetic Resonance Imaging
Weights and Measures
Datasets

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Zhang, P., Wee, C. Y., Niethammer, M., Shen, D., & Yap, P. T. (2013). Large deformation image classification using generalized locality-constrained linear coding. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Pt 1 ed., Vol. 16, pp. 292-299)

Large deformation image classification using generalized locality-constrained linear coding. / Zhang, Pei; Wee, Chong Yaw; Niethammer, Marc; Shen, Dinggang; Yap, Pew Thian.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 16 Pt 1. ed. 2013. p. 292-299.

Research output: Chapter in Book/Report/Conference proceedingChapter

Zhang, P, Wee, CY, Niethammer, M, Shen, D & Yap, PT 2013, Large deformation image classification using generalized locality-constrained linear coding. in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 1 edn, vol. 16, pp. 292-299.
Zhang P, Wee CY, Niethammer M, Shen D, Yap PT. Large deformation image classification using generalized locality-constrained linear coding. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 1 ed. Vol. 16. 2013. p. 292-299
Zhang, Pei ; Wee, Chong Yaw ; Niethammer, Marc ; Shen, Dinggang ; Yap, Pew Thian. / Large deformation image classification using generalized locality-constrained linear coding. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 16 Pt 1. ed. 2013. pp. 292-299
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