Computer Aided Diagnosis System Feature Extraction of Alzheimer Disease Using MFCC

References

• Ashburner, J., & Friston, K. J. (2000). Voxel-based morphometry—The methods. NeuroImage, 11, 805–821. doi:10.1006/nimg.2000.0582.
   PubMed Web of Science ®Google Scholar
• Alzheimers Association (2012). 2012 Alzheimer's disease facts and figures. Alzheimer's & Dementia, 8, 131–168. doi:10.1016/j.jalz.2012.02.001.
   Google Scholar
• Chu, C. -Y. C. (2009). Pattern recognition and machine learning for magnetic resonance images with kernel methods. Thesis submitted for the degree of Doctor of Philosophy, University College London.
   Google Scholar
• Dessouky, M. M., Elrashidy, M. A., Taha, T. E., & Abdelkader, H. M. (2013). Selecting and extracting effective features for automated diagnosis of Alzheimer's disease. International Journal of Computer Applications, 81, 17–28. doi:10.5120/14000-2039.
   Google Scholar
• Devi, M. R., & Ravichandran, T. (2013). A novel approach for speech feature extraction by Cubic-Log compression in MFCC. International Conference on Pattern Recognition, Informatics and Mobile Engineering.
   Google Scholar
• Duda, R. O., Hart, P. E., Stork, D. G., Pattern Classification.
   Google Scholar
• Ferri, C. P., Sousa, R., Albanense, E., Ribeiro, W. S., & Honyashiki, M. (2009). World Alzheimer Report 2009.
   Google Scholar
• Gaikwad, S., Gawali, B., Yannawar, P., & Mehrotra, S. (2011). Feature extraction using fusion MFCC for continuous Marathi speech recognition. India Conference (INDICON).
   Google Scholar
• Ittichaichareon, C., Suksri, S., Yingthawornsuk, T., Speech recognition using MFCC. International Conference on Computer Graphics, Simulation and Modeling (ICGSM'2012) July 28–29, 2012 Pattaya (Thailand).
   Google Scholar
• Loughran, R., Walker, J., O'Neill, M., & O'Farrell, M. (2008). The use of Mel-frequency cepstral coefficients in musical instrument identification. International Computer Music Conference (ICMC).
   Google Scholar
• Marcus, D. S., Wang, T. H., Parker, J., Csernansky, J. G., Morris, J. C., & Buckner, R. L. (2007). Open access series of imaging studies (OASIS): Cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. Journal of Cognitive Neuroscience, 19, 1498–1507. doi:10.1162/jocn.2007.19.9.1498.
   PubMed Web of Science ®Google Scholar
• Martin, B., & Juliet, V. (2010). Extraction of feature from the acoustic activity of RPW using MFCC. Recent Advances in Space Technology Services and Climate Change (RSTSCC), 194–197.
   Google Scholar
• MATLAB Reshape Function. Retrieved from http://www.mathworks.com/help/matlab/ref/reshape.html.
   Google Scholar
• McKhann, G. M., Knopman, D. S., Chertkow, H. H., Hyman, B. T., Jack, C. R., Kawas, C. H., & … Phelps, C. H. (2011). The diagnosis of dementia due to Alzheimer's disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimer's & Dementia, 7, 263–269. doi:10.1016/j.jalz.2011.03.005.
   PubMed Web of Science ®Google Scholar
• Molau, S., Pitz, M., Schlüter, R., & Ney, H. (2001). Computing Mel-frequency cepstral coefficients on the power spectrum. IEEE International Conference on Acoustics, Speech, and Signal Processing, 1, 73–76.
   Google Scholar
• Morgado, P. (September 2012). Automated diagnosis of Alzheimer's disease using PET images. MSc thesis at Electrical and Computer Engineering Dep., Higher Technical institute, Technical University of Lisbon.
   Google Scholar
• OASIS Database. Retrieved from http://www.oasis-brains.org/.
   Google Scholar
• Shih, F. Y. (2010). Image processing and pattern recognition fundamentals and techniques. Hoboken, NJ: IEEE John Wiley & Sons.
   Google Scholar
• SPM8. Retrieved from http://www.fil.ion.ucl.ac.uk/spm/.
   Google Scholar
• Tawfik, N., Eldin, M., Dessouky, M., AbdEl-samie, F., Processing of corneal images with a cepstral approach. The 23rd international conference on Computer Theory and Applications (ICCTA 2013), 29–31 Oct., 2013 Alexandria, Egypt.
   Google Scholar
• Theodoridis, S., & Koutroumbas, K. (2009). Pattern recognition (4th ed.). Amsterdam: Elsevier.
   Google Scholar
• Tiwari, V. (2010). MFCC and its applications in speaker recognition. International Journal on Emerging Technologies, 1, 19–22.
   Google Scholar
• VBM8. Retrieved from http://dbm.neuro.uni-jena.de/vbm/.
   Google Scholar
• Wimo, A., & Prince, M. (September 2010). World Alzheimer Report 2010: The global economic impact of dementia.
   Google Scholar
• Zhao, H. H., Zhao, K. K., Liu, H. H., & Yu, F. (February 2012). Improved MFCC feature extraction combining symmetric ICA algorithm for robust speech recognition. Journal of Multimedia, 7(1), doi:10.4304/jmm.7.1.74-81.
   Google Scholar