Exploring multifractal-based features for mild Alzheimer's disease classification.

Journal: Magnetic resonance in medicine

PMID: 26193379

Abstract

PURPOSE: Multifractal applications to resting state functional MRI (rs-fMRI) time series for diagnosing Alzheimer's disease (AD) are still limited. We aim to address two issues: (I) if and what multifractal features are sufficiently discriminative to detect AD from the healthy; (II) if AD classification could be further improved by combining multifractal features with traditional features in this field.

Authors

Huangjing Ni

School of Electronic Science and Engineering, Nanjing University, Nanjing, China.
Luping Zhou

School of Computer Science and Software Engineering, University of Wollongong, Wollongong, NSW, Australia.
Xinbao Ning

School of Electronic Science and Engineering, Nanjing University, Nanjing, China.
Lei Wang

Department of Nursing, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.

Keywords

Aged Algorithms Alzheimer Disease Brain Diagnosis, Differential Female Fractals Humans Image Enhancement Image Interpretation, Computer-Assisted Magnetic Resonance Imaging Male Pattern Recognition, Automated Reproducibility of Results Sensitivity and Specificity Severity of Illness Index Subtraction Technique Support Vector Machine

External Resources

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Exploring multifractal-based features for mild Alzheimer's disease classification.

Abstract

Authors

Keywords

External Resources

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