ICA-based automatic classification of magnetic resonance images from ADNI data

Authors:
Wenlu Yang;Xinyun Chen;Hong Xie;Xudong Huang
Affiliations:
Department of Electronic Engineering, Shanghai Maritime University, Shanghai, China and Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA;Department of Electronic Engineering, Shanghai Maritime University, Shanghai, China;Department of Electronic Engineering, Shanghai Maritime University, Shanghai, China;Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
Venue:
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part III
Year:
2010

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Abstract

This paper proposes a novel method of automatic classification of magnetic resonance images based on independent component analysis (ICA). The ICA-based method is composed of three steps. First, all magnetic resonance imaging (MRI) scans are aligned and normalized by statistical parametric mapping. Then FastICA is applied to the preprocessed images for extracting specific neuroimaging components as potential classifying feature. Finally, the separated independent coefficients are fed into a classifying machine that discriminates among Alzheimer's patients, and mild cognitive impairment, and control subjects. In this study, the MRI data is selected from the Alzheimer's Disease Neuroimaging Initiative databases. The experimental results show that our method can successfully differentiate subjects with Alzheimer's disease and mild cognitive impairment from normal controls.