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Classification of ADNI PET images via regularized 3D functional data analysis

Classification of ADNI PET images via regularized 3D functional data analysis We propose a penalized Haar wavelet approach for the classification of three-dimensional (3D) brain images in the framework of functional data analysis, which treats each entire 3D brain image as a single functional input, thus automatically takes into account the spatial correlations of voxel-level imaging measures. We validate the proposed approach through extensive simulations and compare its classification performance with other commonly used machine learning methods, which show that the proposed method outperforms other methods in both classification accuracy and identification of the relevant voxels. We then apply the proposed method to the practical classification problems for Alzheimer's disease using positron emission tomography images obtained from the Alzheimer's Disease Neuroimaging Initiative database to highlight the advantages of our approach. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biostatistics & Epidemiology Taylor & Francis

Classification of ADNI PET images via regularized 3D functional data analysis

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Classification of ADNI PET images via regularized 3D functional data analysis

Abstract

We propose a penalized Haar wavelet approach for the classification of three-dimensional (3D) brain images in the framework of functional data analysis, which treats each entire 3D brain image as a single functional input, thus automatically takes into account the spatial correlations of voxel-level imaging measures. We validate the proposed approach through extensive simulations and compare its classification performance with other commonly used machine learning methods, which show that the...
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Publisher
Taylor & Francis
Copyright
© 2017 International Biometric Society – Chinese Region
ISSN
2470-9379
eISSN
2470-9360
DOI
10.1080/24709360.2017.1280213
Publisher site
See Article on Publisher Site

Abstract

We propose a penalized Haar wavelet approach for the classification of three-dimensional (3D) brain images in the framework of functional data analysis, which treats each entire 3D brain image as a single functional input, thus automatically takes into account the spatial correlations of voxel-level imaging measures. We validate the proposed approach through extensive simulations and compare its classification performance with other commonly used machine learning methods, which show that the proposed method outperforms other methods in both classification accuracy and identification of the relevant voxels. We then apply the proposed method to the practical classification problems for Alzheimer's disease using positron emission tomography images obtained from the Alzheimer's Disease Neuroimaging Initiative database to highlight the advantages of our approach.

Journal

Biostatistics & Epidemiology – Taylor & Francis

Published: Jan 1, 2017

Keywords: Classification; elastic net; functional logistic regression; Haar wavelets; PCA; PET

References

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