Classification of Alzheimer Disease Using Dual Tree Complex Wavelet Transform and Minimum Redundant & Maximum
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초록
Multivariate analysis algorithms have been frequently used tool for predicting Alzheimer disease (AD). An efficient early prediction and diagnosis of AD and Mild Cognitive Impairment (MCI) from Healthy Controls (HC) is always very important. Early preventive care could help to degenerate its risk factors gradually in the elderly and the stable life will be guaranteed. The noninvasive biomarker of Magnetic Resonance (MR) images are used here because morphometric difference and cerebral atrophy could be realized. And we also used MR image slices after subcortical segmentation by using FreeSurfer tool. A novel approach is applied for predicting AD from HC using dual tree complex wavelet coefficients extraction, principal components of min-redundancy and max relevance feature subset selection. The prediction accuracy of the novel proposed method is yielded up to 87.07±2.71% with high specificity, sensitivity about 91.02±3.38%, 83.10±3.4% respectively. The experimental results show that the performance of our proposed method is better than that of conventional ones.
Abstract
Multivariate analysis algorithms have been frequently used tool for predicting Alzheimer disease (AD). An efficient early prediction and diagnosis of AD and Mild Cognitive Impairment (MCI) from Healthy Controls (HC) is always very important. Early preventive care could help to degenerate its risk factors gradually in the elderly and the stable life will be guaranteed. The noninvasive biomarker of Magnetic Resonance (MR) images are used here because morphometric difference and cerebral atrophy could be realized. And we also used MR image slices after subcortical segmentation by using FreeSurfer tool. A novel approach is applied for predicting AD from HC using dual tree complex wavelet coefficients extraction, principal components of min-redundancy and max relevance feature subset selection. The prediction accuracy of the novel proposed method is yielded up to 87.07±2.71% with high specificity, sensitivity about 91.02±3.38%, 83.10±3.4% respectively. The experimental results show that the performance of our proposed method is better than that of conventional ones.
참고문헌 (18)
- J. Ashburner, "Multivariate models of inter-subject anatomical variability", NeuroImage, 56, pp.422~439 (2011)
- N. Koutsouleris, "Use of neuroanatomical pattern classification to identify subjects in at-risk mental states of psychosis and predict disease transition", Arch. Gen. Psychiatry, 66, pp.700~712 (2009)
- M. Weygandt, "MRI pattern recognition in multiple sclerosis normal-appearing brain areas", PLoS One, 6, pp.e21138~ (2011)
- M. Chupin, "Fully automatic hippocampus segmentation and classification in Alzheimer’s disease and mild cognitive impairment applied on data from ADNI", Hippocampus, 19, pp.579~587 (2009)
- S. Klöppel, "Automatic classification of MR scans in Alzheimer's disease", Brain, 131, pp.681~689 (2008)
- C. Ecker, "Describing the brain in autism in five dimensions--magnetic resonance imaging assisted diagnosis of autism spectrum disorder using a multiparameter classification approach", J. Neurosci., 30, pp.10612~10623 (2010)
- R.A. Yotter, "Local cortical surface complexity maps from spherical harmonic reconstructions", NeuroImage, 56, pp.961~973 (2011)
- Z. Sankari, "Probabilistic neural networks for EEG-based diagnosis of Alzheimer's disease using conventional and wavelet coherence", Journal of Neuroscience Methods, 197(1), pp.165~170 (2011)
- S. Chaplot, "Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network", Biomedical Signal Processing and Control, 1(1), pp.86~92 (2006)
- C.H. Moritz, "Whole-brain functional MR imaging activation from finger tapping task examined with independent component analysis", Am. J. Neuroradiol., 21(9), pp.1629~1635 (2000)
- R.N. Bracewell, "The Fourier Transform and its Applications" (1999), McGraw-Hill
- El-Dahshan, "Hybrid intelligent techniques for MRI brain images classification Digital", Signal Process, 20(2), pp.433~441 (2000)
- Y. Zhang, "A hybrid method for MRI brain image classification", Exp. Syst. Appl., 38, pp.10049~10053 (2011)
- A. Barri, "The near shift-invariance of the dual-tree complex wavelet transform revisited", Journal of Mathematical Analysis and Applications, 389(2), pp.1303~1314 (2012)
- Fischl, "Whole Brain Segmentation:Automated Labeling of Neuroanatomical Structures in the Human Brain", Neuron, 33(3), pp.341~355 (2002)
- I. W. Selesnick, "The dual-tree complex wavelet transform", IEEE Signal Processing Magazine, 22(6), pp.1053~5888 (2005)
- H. Peng, "Feature Selection Based on Mutual Information:Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy", IEEE transactions on pattern analysis and machine intelligence, 27(8), pp.1226~1238 (2005)
- C. Burges, "Tutorial on support vector machine for pattern recognition", Data Mining Knowl. Discov, 2(2), pp.121~167 (1998)