Effective Features Extracting Approach Using MFCC for Automated Diagnosis of Alzheimer’s Disease
Abstract
This paper proposes a Computer Aided Diagnosis (CAD) system for extracting the most significant features of Alzheimer’s disease (AD) using the Mel-Scale Frequency Cepstral Coefficients (MFCC). The features extracted from AD MRI images are effective features used for the diagnosis of AD. To test the proposed model, the database used Open Access Series of Imaging Studies (OASIS) database for Magnetic Resonance Imaging (MRI). First, the images are transformed from 3-D to 1-D, then it will be processed using the approach given in [1] for feature reduction. In addition, the MFCC algorithm will be used to extract the effective features, then applying a Linear Support Vector Machine (SVM) classifier for classification step. This paper presents the comparison study between the approach given in [1], the approach using MFCC only, and using proposed approach. The obtained results provides excellent results using the proposed algorithm with high accuracy using small number of significant extracted features.
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M.M.Dessouky, M.A.Elrashidy, T.E.Taha, and H.M Abdelkader, “Selecting and Extracting Effective Features for Automated Diagnosis of Alzheimer’s Disease”, International Journal of Computer Applications, Vol. 81 – No.4, 2013, pp 17-28.
P. Morgado, “Automated Diagnosis of Alzheimer’s Disease using PET Images”, MSc thesis at Electrical and Computer Engineering Dep., Higher technical institute, Technical University of Lisbon, September 2012.
C. P. Ferri, R. Sousa, E. Albanense, W. s. Ribeiro, and M. Honyashiki, “World Alzheimer Report 2009,” 2009.
A. Wimo and M. Prince, “World Alzheimer Report 2010: The global economic impact of dementia,” September 2010.
A. Association, “2012 Alzheimer’s disease facts and figures,” Alzheimer’s and Dementia: The Jthenal of the Alzheimer’s Association, vol. 8, no. 2, pp. 131–168, 2012.
S. Gaikwad, B. Gawali , P. Yannawar , and S. Mehrotra, “Feature Extraction Using Fusion MFCC For Continuous Marathi Speech Recognition”, India Conference (INDICON), 2011.
C. Ittichaichareon, S. Suksri and T. Yingthawornsuk, “Speech Recognition using MFCC”, International Conference on Computer Graphics, Simulation and Modeling (ICGSM'2012) July 28-29, 2012 Pattaya (Thailand)
S. Molau, M. Pitz, R. Schl¨uter, and H. Ney, “Computing Mel-Frequency Cepstral Coefficients on the Power Spectrum”, IEEE International Conference on Acoustics, Speech, and Signal Processing. Vol.1, 2001.
H. Zhao, K. Zhao, and H. Liu, “Improved MFCC Feature Extraction Combining Symmetric ICA Algorithm for Robust Speech Recognition”, JOURNAL OF MULTIMEDIA, VOL. 7, NO. 1, FEBRUARY 2012.
R. Loughran, J. Walker, M. O’Neill, and M. O’Farrell, “The Use of Mel-frequency Cepstral Coefficients in Musical Instrument Identification”, International Computer Music Conference (ICMC), 2008.
N. Tawfik, M. Eldin, M. Dessouky, and F. AbdEl-samie, “processing of Corneal Images With a Cepstral Approach”, ICCTA,2013.
OASIS Database: http://www.oasis-brains.org/
VBM8: http://dbm.neuro.uni-jena.de/vbm/
Ashburner J, Friston KJ. Voxel-based morphometry—the methods. Neuroimage 2000;11:805–21.
Adam M. Brickman , Christian Habeck, Eric Zarahn, Joseph Flynn, Yaakov Stern, “Structural MRI covariance patterns associated with normal aging and neuropsychological functioning”, Neurobiology of Aging, 2006
SPM8: http://www.fil.ion.ucl.ac.uk/spm/
John Ashburner and Karl J. Friston, “Voxel-Based Morphometry—The Methods”, NeuroImage 11, 805–821 (2000).
B. Martin, and V. Juliet, “Extraction of Feature from the Acoustic Activity of RPW using MFCC”, Recent Advances in Space Technology Services and Climate Change (RSTSCC), 2010, pp. 194-197.
M.R.Devi, and T.Ravichandran, “A novel approach for speech feature extraction by Cubic-Log compression in MFCC”, International Conference on Pattern Recognition, Informatics and Mobile Engineering, 2013.
V. Tiwari, “MFCC and its applications in speaker recognition”, International Journal on Emerging Technologies 1(1): 19-22(2010), pp. 19-22.
Chia-Yueh C. CHU, “Pattern recognition and machine learning for magnetic resonance images with kernel methods”, thesis submitted for the degree of Doctor of Philosophy, University College London, 2009.
Katherine R. Gray, “Machine learning for image-based classification of Alzheimer's disease”, thesis submitted for the degree of Doctor of Philosophy, Department of Computing, Imperial College London, 2012.
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