New approach for automatic classification of Alzheimer's disease, mild cognitive impairment and healthy brain magnetic resonance images

Healthcare Technology Letters

article

Salim Lahmiri¹  Mounir Boukadoum¹ 

[1] Department of Computer Science, University of Quebec at Montreal

关键词: image classification;    cognition;    diseases;    biomedical MRI;    support vector machines;    medical image processing;    clinical applications;    cross-validation technique;    AD classification;    SVM;    support vector machines;    Hurst exponents;    fractal multiscale analysis;    MCI;    healthy brain image classification;    fractal object;    healthy brain magnetic resonance images;    mild cognitive impairment;    Alzheimer disease;   

DOI  :  10.1049/htl.2013.0022

学科分类：肠胃与肝脏病学

来源: Wiley

【 摘 要 】

Explored is the utility of modelling brain magnetic resonance images as a fractal object for the classification of healthy brain images against those with Alzheimer's disease (AD) or mild cognitive impairment (MCI). More precisely, fractal multi-scale analysis is used to build feature vectors from the derived Hurst's exponents. These are then classified by support vector machines (SVMs). Three experiments were conducted: in the first the SVM was trained to classify AD against healthy images. In the second experiment, the SVM was trained to classify AD against MCI and, in the third experiment, a multiclass SVM was trained to classify all three types of images. The experimental results, using the 10-fold cross-validation technique, indicate that the SVM achieved 97.08% ± 0.05 correct classification rate, 98.09% ± 0.04 sensitivity and 96.07% ± 0.07 specificity for the classification of healthy against MCI images, thus outperforming recent works found in the literature. For the classification of MCI against AD, the SVM achieved 97.5% ± 0.04 correct classification rate, 100% sensitivity and 94.93% ± 0.08 specificity. The third experiment also showed that the multiclass SVM provided highly accurate classification results. The processing time for a given image was 25 s. These findings suggest that this approach is efficient and may be promising for clinical applications.

【 授权许可】

CC BY|CC BY-ND|CC BY-NC|CC BY-NC-ND