title: New approach for automatic classification of Alzheimer's disease, mild cognitive impairment and healthy brain magnetic resonance images.
abstract: 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.
authors.affiliation: Department of Computer Science , University of Quebec at Montreal , Montreal , Quebec , H3C 3P8 , Canada.
authors.forename: Salim
authors.initials: S
authors.lastname: Lahmiri
authors.affiliation: Department of Computer Science , University of Quebec at Montreal , Montreal , Quebec , H3C 3P8 , Canada.
authors.forename: Mounir
authors.initials: M
authors.lastname: Boukadoum
keywords: AD classification; Alzheimer disease; Hurst exponents; MCI; SVM; biomedical MRI; clinical applications; cognition; cross-validation technique; diseases; fractal multiscale analysis; fractal object; healthy brain image classification; healthy brain magnetic resonance images; image classification; medical image processing; mild cognitive impairment; support vector machines