Abstract
Computer aided diagnosis of Alzheimer’s disease from MRI brain images has drawn the attention of pattern recognition and machine learning research community in last few years. Relevant feature extraction from such MRI images is one of the challenging issues in decision system. Recently dominant values obtained from 6 level decomposition using Slantlet transform are used to construct such features. In this paper, we have determined relevant features using first order statistics on coefficients obtained from Slantlet transform. We have compared the performance in terms of 8 well known and a combined performance measures. Experimental results on publicly available MRI dataset show that proposed method outperforms the dominant value based features extracted using Slantlet transform in terms of both individual and combined performance measures.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Albert, M., DeCarli, C., DeKosky, S., De Leon, M., Foster, N., Fox, N., et al.: The Use of MRI and PET for Clinical Diagnosis of Dementia and Investigation of Cognitive Impairment: A Consensus Report, Prepared by the Neuroimaging Work Group of the Alzheimer’s Association (2004)
Mortiz, C., Haughton, V., Cordes, D., Quigley, M., Meyerand, M.: Whole-brain functional MR imaging activation from finger tapping task examined with independent component analysis. American Journal of Neuroradiology 21, 1629–1635 (2000)
Bracewell, R.: The Fourier Transform and its Applications, 3rd edn. McGraw-Hill, New York (1999)
Chaplot, S., Patnaik, L., Jagannathan, N.: Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomed. Signal Process. and Control 1, 86–92 (2006)
Dahshan, E.-S., Hosny, T., Salem, A.-B.: A hybrid technique for automatic MRI brain images classification. Digital Signal Processing 20, 433–441 (2010)
Maitra, M., Chatterjee, A.: A Slantlet transform based intelligent system for magnetic resonance brain image classification. Biomedical Signal Processing and Control 1, 299–306 (2006)
Haralick, R., Shanmugan, K., Dinstein, I.: Textural Features for Image Classification. IEEE Transactions on Systems: Man, and Cybernetics SMC 3, 610–621 (1973)
Materka, A., Strzeleck, M.: Texture Analysis Methods - A Review. COST B11 report, Technical University of Lodz, Brussels (1998)
Begg, R., Palaniswami, M., Owen, B.: Support vector machines for automated gait classification. IEEE Transactions on Biomedical Engineering 52(5), 828–838 (2005)
Selesnick, I.W.: The Slantlet Transform. IEEE Transactions on Signal Processing 47(5) (May 1999)
Bellman, R.: Adaptive control processes: A guided tour. Princeton University Press (1961)
Gonzalez, R., Woods, R.: Wavelet and Multiresolution Processing. In: Digital Image Processing, 2nd edn., pp. 349–408. Pearson Education (2004)
Chen, J., Kundu, A.: Rotation and grey-scale transform invariant texture identification using wavelet decomposition and HMM. IEEE Trans. PAMI 16(2), 208–214 (1994)
Alpert, B., Beylkin, G., Coifman, R., Rokhlin, V.: Wavelet-like bases for the fast solution of second-kind integral equations. SIAM J. Sci. Comput. 14(1), 159–184 (1993)
Aldroubi, A., Unser, M., Eden, M.: Discrete spline filters for multiresolution and wavelets of I2. SIAM Journal on Mathematical Analysis 25(5), 1412–1432 (1994)
Johnson, K., Becker, J.: The whole brain atlas. In: Harvard Medical School (accessed 1995), http://www.med.harvard.edu/aanlib/home.html
George, A., Leon, M., Golomb, J., Kluger, A., Convit, A.: Imaging the brain in dementia: expensive and futile? Am. J. Neuroradiol. 18, 1847–1850 (1997)
Laakso, M., Frisoni, G., Kononen, M., Mikkonen, M., Beltramello, A., Geroldi, C., Bianchetti, A., Trabucchi, M., Soininen, H., Aronen, H.: Hippocampus and entorhinal cortex in frontotemporal dementia and Alzheimer’s disease: a morphometric MRI study. Biol. Psychiat. 47, 1056–1063 (2000)
Erkinjuntti, T., Lee, D., Gao, F., Steenhuis, R., Eliasziw, M., Fry, R., Merskey, H., Hachinski, V.: Temporal lobe atrophy on magnetic resonance imaging in the diagnosis of early Alzheimer’s disease. Arch. Neurol. 50, 305–310 (1993)
Duin, R., Juszcak, P., Paclik, P., Pekalska, E., De Ridder, D., Tax, D.: Prtools, a matlab toolbox for pattern recognition (accessed 2004), http://www.prtools.org
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Aggarwal, N., Bharti, Agrawal, R.K. (2012). Computer Aided Diagnosis of Alzheimer’s Disease from MRI Brain Images. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2012. Lecture Notes in Computer Science, vol 7325. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31298-4_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-31298-4_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31297-7
Online ISBN: 978-3-642-31298-4
eBook Packages: Computer ScienceComputer Science (R0)