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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.
References
-
-
1)
-
7. Vapnik, V.: ‘The nature of statistical learning theory’ (Springer, Berlin, 1995).
-
2)
-
3)
-
4)
-
1. Chincarini, A., Bosco, P., Calvini, P., et al: ‘Local MRI analysis approach in the diagnosis of early and prodromal Alzheimer's disease’, NeuroImage, 2011, 58, pp. 469–480 (doi: 10.1016/j.neuroimage.2011.05.083).
-
5)
-
6)
-
5. Cuingnet, R., Gerardin, E., Tessieras, J., et al: ‘Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database’, NeuroImage, 2011, 56, pp. 766–781 (doi: 10.1016/j.neuroimage.2010.06.013).
-
7)
-
8)
-
C.-K. Peng ,
S.V. Buldyrev ,
S. Havlin
.
Mosaic organization of DNA nucleotides.
Phys. Rev. E
,
2 ,
1685 -
1689
-
9)
-
6. Di Matteo, T.: ‘Multi-scaling in finance’, Quant. Financ., 2007, 7, (1), pp. 21–36 (doi: 10.1080/14697680600969727).
-
10)
-
3. Liu, M., Zhang, D., Shen, D.: ‘Ensemble sparse classification of Alzheimer's disease’, NeuroImage, 2012, 60, pp. 1106–1116 (doi: 10.1016/j.neuroimage.2012.01.055).
-
11)
-
8. Lahmiri, S., Boukadoum, M.: ‘Alzheimer disease detection in brain magnetic resonance images using multi-scale fractal analysis’, ISRN Radiol., 2013, .
-
12)
-
2. Zhang, D., Wang, Y., Zhou, L., Zhou, L., Shen, D.: ‘The Alzheimer's disease neuroimaging initiative: ‘multimodal classification of Alzheimer's disease and mild cognitive impairment’, NeuroImage, 2011, 55, pp. 856–867 (doi: 10.1016/j.neuroimage.2011.01.008).
-
13)
-
15. Rees, D.G.: ‘Essential statistics’ (Chapman & Hall/CRC, 2001, 4th edn.).
-
14)
-
16. Brown, M.B., Forsythe, A.B.: ‘Robust tests for the equality of variances’, J. Am. Stat. Assoc., 1974, 69, pp. 364–367 (doi: 10.1080/01621459.1974.10482955).
-
15)
-
17. Brown, M.B., Forsythe, A.B.: ‘The small sample behavior of some test statistics which test the equality of several means’, Technometrics, 1974, 16, pp. 129–132 (doi: 10.1080/00401706.1974.10489158).
-
16)
-
C.W. Hsu ,
C.J. Lin
.
A comparison of methods for multiclass support vector machines.
IEEE Trans. Neural Netw.
,
2 ,
415 -
425
-
17)
-
4. Zhang, D., Shen, D.: ‘Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease’, NeuroImage, 2012, 59, pp. 895–907 (doi: 10.1016/j.neuroimage.2011.09.069).
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