In literature, features based on First and Second Order Statistics that characterizes textures are used for classification of images. Features based on statistics of texture provide far less number of relevant and distinguishable features in comparison to existing methods based on wavelet transformation. In this paper, we investigated performance of texture-based features in comparison to wavelet-based features with commonly used classifiers for the classification of Alzheimer’s disease based on T2-weighted MRI brain image. The performance is evaluated in terms of sensitivity, specificity, accuracy, training and testing time. Experiments are performed on publicly available medical brain images. Experimental results show that the performance with First and Second Order Statistics based features is significantly better in comparison to existing methods based on wavelet transformation in terms of all performance measures for all classifiers.
KeywordsAlzheimer’s DiseaseMagnetic Resonance ImagingFeature ExtractionDiscrete Wavelet TransformFirst and Second Order Statistical Features
WHO, “Priority Medicines for Europe and the World,” World Health Organization, 2005.
NIH, “Progress Report on Alzheimer’s Disease 20042005,” National Institutes of Health (NIH), Bethesda, 2005.
D. S. Knopman, S. T. DeKosky, J. L. Cummings, H. Chui, J. Corey-Bloom, N. Relkin, G. W. Small, B. Miller and J. C. Stevens, “Practice Parameter: Diagnosis of Dementia (an Evidence-Based Review): Report of the Quality Standards Subcommittee of the American Academy of Neurology,” Neurology, Vol. 56, 2001, pp. 1143-1153. doi:10.1212/WNL.56.9.1143
C. M. Bottino, C. C. Castro, R. L. Gomes, C. A. Buchpiguel, R. L. Marchetti and M. R. Neto, “Volumetric MRI Measurements Can Differentiate Alzheimer’s Disease, Mild Cognitive Impairment, and Normal Aging,” International Psychogeriatric, Vol. 14, No. 1, 2002, pp. 59-72. doi:10.1017/S1041610202008281
K. M. Gosche, J. A. Mortimer, C. D. Smith, W. R. Markesbery and D. A. Snowdon, “Hippocampal Volume As an Index of Alzheimer Neuropathology: Findings from the Nun Study,” Neurology, Vol. 58, No. 10, 2002, pp. 1476-1482. doi:10.1212/WNL.58.10.1476
L. A. Van de Pol, A. Hensel, W. M. Van der Flier, P. Visser, Y. A. Pijnenburg, F. Barkhof, H. J. Gertz and P. Scheltens, “Hippocampal Atrophy on MRI in Frontotemporal Lobar Degeneration and Alzheimer’s Disease,” Journal of Neurology, Neurosurgery & Psychiatry, Vol. 77, No. 4, 2006, pp. 439-442. doi:10.1136/jnnp.2005.075341
A. George, M. D. Leon, J. Golomb, A. Kluger and A. Convit, “Imaging the Brain in Dementia: Expensive and Futile?” American Journal of Neuroradiology, Vol. 18, 1997, pp. 1847-1850.
M. P. Laakso, G. B. Frisoni, M. Kononen, M. Mikkonen, A. Beltramello, C. Geroldi, A. Bianchetti, M. Trabucchi, H. Soininen and H. J. Aronen, “Hippocampus and Entorhinal Cortex in Frontotemporal Dementia and Alzheimer’s Disease: A Morphometric MRI Study,” Biological Psychiatry, Vol. 47, No. 12, 2000, pp. 1056-1063. doi:10.1016/S0006-3223(99)00306-6
M. J. De Leon, J. Golomb, A. E. George, A. Convit, C. Y. Tarshish, T. McRae, S. De Santi, G. Smith, S. H. Ferris and M. Noz, “The Radiologic Prediction of Alzheimer’s Disease: The Atrophic Hippocampal Formation,” American Journal of Neuroradiology, Vol. 14, 1993, pp. 897-906.
T. Erkinjuntti, D. H. Lee, F. Gao, R. Steenhuis, M. Eliasziw, R. Fry, H. Merskey and V. C. Hachinski, “Temporal Lobe Atrophy on Magnetic Resonance Imaging in the Diagnosis of Early Alzheimer’s Disease,” Archives of Neurology, Vol. 50, 1993, pp. 305-310. doi:10.1001/archneur.1993.00540030069017
A. I. Holodny, R. Waxman, A. E. George, H. Rusinek, A. J. Kalnin and M. de Leon, “MR Differential Diagnosis of Normal-Pressure Hydrocephalus and Alzheimer Disease: Significance of Perihippocampal Fissures,” American Journal of Neuroradiology, Vol. 19, No. 5, 1998, pp. 813-819.
M. Albert, C. DeCarli, S. DeKosky, M. De Leon, N. L. Foster, N. Fox, 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.
C. H. Mortiz, V. M. Haughton, D. Cordes, M. Quigley and M. E. Meyerand, “Whole-Brain Functional MR Imaging Activation from Finger Tapping Task Examined with Independent Component Analysis,” American Journal of Neuroradiology, Vol. 21, No. 9, 2000, pp. 1629-1635.
R. N. Bracewell, “The Fourier Transform and Its Applications,” 3rd Edition, McGraw-Hill, New York, 1999.
S. Chaplot, L. M. Patnaik and N. R. Jagannathan, “Classification of Magnetic Resonance Brain Images Using Wavelets as Input to Support Vector Machine and Neural Network,” Biomedical Signal Processing and Control, Vol. 1, No. 1, 2006, pp. 86-92. doi:10.1016/j.bspc.2006.05.002
E.-S. A. Dahshan, T. Hosny and A.-B. M. Salem, “A Hybrid Technique for Automatic MRI Brain Images Classification,” Digital Signal Processing, Vol. 20, No. 2, 2010, pp. 433-441. doi:10.1016/j.dsp.2009.07.002
R. M. Haralick, K. Shanmugan and I. Dinstein, “Textural Features for Image Classification,” IEEE Transactions on Systems: Man, and Cybernetics SMC, Vol. 3, 1973, pp. 610-621. doi:10.1109/TSMC.1973.4309314
A. Materka and M. Strzeleck, “Texture Analysis Methods —A Review,” Institute of Electronics, Technical University of Lodz, Brussels, 1998.
R. K. Begg, M. Palaniswami and B. Owen, “Support Vector Machines for Automated Gait Classification,” IEEE Transactions on Biomedical Engineering, Vol. 52, No. 5, 2005, pp. 828-838. doi:10.1109/TBME.2005.845241
I. T. Jolliffe, “Principal Component Analysis,” SpringerVerlag, New York, 1986.
S. G. Mallat, “A Theory of Multiresolution Signal Decomposition: The Wavelet Representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 11, No. 7, 1980, pp. 674-693. doi:10.1109/34.192463
R. C. Gonzalez and R. E. Woods, “Wavelet and Multiresolution Processing,” In: Digital Image Processing, 2nd Edition, Pearson Education, Upper Saddle River, 2004, pp. 349-408.
J. Koenderink, “The Structure of Images,” Biological Cybernetics, Vol. 50, No. 5, 1984, pp. 363-370. doi:10.1007/BF00336961
R. A. Lerski, K. Straughan, L. R. Schad, D. Boyce, S. Bluml and I. Zuna, “MR Image Texture Analysis—An approach to Tissue Characterization,” Magnetic Resonance Imaging, Vol. 11, No. 6, 1993, pp. 873-887. doi:10.1016/0730-725X(93)90205-R
S. Haykin, “Neural Networks: A Comprehensive Foundation,” Prentice Hall, Upper Saddle River, 1999.
R. O. Duda, P. E. Hart and D. G. Stork, “Pattern Classification,” Wiley, New York, 2001.
K. A. Johnson and J. A. Becker, “The Whole Brain Atlas,” 1995. http://www.med.harvard.edu/aanlib/home.html
R. Duin, P. Juszcak, P. Paclik, E. Pekalska, D. De Ridder and D. Tax, “Prtools, a Matlab Toolbox for Pattern Recognition,” Delft University of Technology, 2004. http://www.prtools.org.
First and Second Order Statistics Features for Classification of Magnetic Resonance Brain Images — Oak Academic Publishing