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Automatic Brain MRI Mining Using Support Vector Machine and Decision Tree

T.R. Sivapriya, Dr.V. Saravanan

Abstract


This paper presents a texture based classification method using Support vector machine and decision tree for diagnosis of dementia. Support Vector Machine has been proved to be an effective classifier in several applications. In this work, a comparison of linear and non-linear kernels of SVM with BPN is investigated. Rules are extracted from a trained SVM which is compared with rules extracted from BPN and C5.0. OASIS dataset is utilized for training and testing of the classifiers. Wavelet based textural features from the brain MRI images are given as input feature vectors for classification. From the analysis it is found that SVM outperforms other classifiers. Rules extracted from the trained SVM improve the comprehensibility of the classifier.

Keywords


SVM, BPN, Decision Tree, MRI.

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References


T.R.Sivapriya, V.Saravanan, Evolutionary decision tree extraction from trained backpropagation network for mining brain mri. International Conference on Information and Communication Technology ,ISBN 978-1-4507-5165-0 ,2010, pp148-152.

C.J.C. Burges. tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2), 955–974, 1998.

C.J.C. Burges, & B. Scholkopf. Improving the accuracy and speed of support vector machines, 1997.

Haralick, Robert M; Shanmugam, , “Dinstein, Textural Features for Image Classification”, IEEE Transactions on Systems, Man and Cybernetics, Vol. No. 3, Issue 6, pps610-621, 1973.

Craft S, Teri L, Edland SD, Kukull WA, Schellenberg G, McCormick WC, Bowen JD, Larson EB. Accelerated decline in apolipoprotein E-epsilon4 homozygotes with Alzheimer's disease. Neurology 1998; 51:149–153. [PubMed: 9674794]

N. Cristianini, J. Shawe-Taylor. An Introduction to Support Vector Machines. Cambridge University Press; 2000.

HA. Crystal , DW. Dickson ,MJ. Sliwinski, RB. Lipton E. Grober , H.Marks-Nelson, P. Antis. Pathological markers associated with normal aging and dementia in the elderly. Ann Neurol 1993;34:566–573.[PubMed: 8215244]

Fan Y, Batmanghelich N, Clark CM, Davatzikos C (2008) Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. Neuroimage 39, 1731-1743.

I. Guyon, A. Elisseeff. An introduction to variable and feature selection. J Mach Learn Res 2003;3:1157–82.

Gilbert G. Walter, Xiaoping Shen, Wavelets and Other Orthogonal Systems (2001)

Haar, Alfred; Zur Theorie der orthogonalen Funktionensysteme. (German) Mathematische Annalen 69 (1910), no. 3, 331–371.

DJ. Hand. Mining medical data. Stat Methods Med Res 2000;9:305–7.

Jirak D, Dezortova M, Taimr P, Hajek M: Texture analysis of human liver. J Magn Reson Imaging 15:68–74, 2002

A.KassnerR.E. Thornhill, Texture Analysis : A Review of Neurologic MR Imaging Applications AJNR Am J Neuroradiol 31:809,May 2010.

Kerut EK, Given M, Giles TD: Review of methods for texture analysis of myocardium from echocardiographic images: a means of tissue characterization. Echocardiography 20:727–736, 2003

Kloppel S, Stonnington CM, Chu C, Draganski B, Scahill RI, Rohrer JD, Fox NC, Jack CR, Jr., Ashburner J, Frackowiak RS (2008) Automatic classification of MR scans in Alzheimer‟s disease. Brain 131, 681-689.

Z. Lao, D. Shen, Z. Xue, B. Karacali, S. M. Resnick, and C. Davatzikos. Morphological classification of brains via high-dimensional shape transformations and machine learning methods. Neuroimage, 21(1):46_57, 2004.

Lerch JP, Pruessner J, Zijdenbos AP, Collins DL, Teipel SJ, Hampel H, Evans AC (2008) Automated cortical thickness measurements from MRI can accurately separate Alzheimer‟s patients from normal elderly controls. Neurobiol Aging 29, 23-30.

Mahmoud-Ghoneim D, ToussaintG,Constans JM, deCertaines JD: Three dimensional texture analysis in MRI: a preliminary evaluation in gliomas. Magn Reson Imaging 21:983–987, 2003

D. Marcus, JC. Morris, AZ. Snyder. A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume. Neuroimage 23, 724-38, 2004.

DS. Marcus, TH. Wang,, J.M. Parker, JG.Csernansky, JC. Morris, RL. Buckner. 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, 2007.

Mathias JM, Tofts PS, Losseff NA: Texture analysis of spinal cord pathology in multiple sclerosis. Magn Reson Med42:929–935, 1999

Mayerhoefer ME, Breitenseher MJ, Kramer J, Aigner N, Hofmann S, Materka A: Texture analysis for tissue discrimination on T1-weighted MR images of the knee joint in a multicenter study: Transferability of texture features and comparison of feature selection methods and classifiers. J Magn Reson Imaging 22:674-680, 2005

JC. Morris, 1993. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 43, 2412b-2414b.

M. Mozer, M. Jordan, & T. Petsche (Eds.), Advances in Neural Information Processing Systems (pp. 475–481). Cambridge, MA: MIT Press,1997.

H. Núñez, C. Angulo, A. Catala, Rule-extraction from Support Vector Machines, The European Symposium on Artificial Neural Networks, Burges, ISBN 2- 930307-02-1, 2002, pp.107-112.

Parpinelli, R., Lopes, H. and Freitas, A., “An Ant Colony Algorithm for Classification Rule Discovery”, Idea Group, 2002.

V. N. Vapnik. The Nature of Statistical Learning Theory. Springer-Verlag, New York, 1995.

E. Osuna, R. Freund, and F. Girosi, “Training support vector machines: Application to face detection,” in Proc. Computer Vision and Pattern Recognition, Puerto Rico, pp. 130-136, 1997.

M. Pontil and A. Verri, “Support vector machines for 3-D object recognition,” IEEE Trans. pattern anal. Machine Intel., vol. 20, pp. 637-646, 1998.

Schilling T, Miroslaw L, Glab G, Smereka M: Towards rapid cervical cancer diagnosis: Automated detection and Classification of pathologic cells in phase-contrast images. Int J Gynecol Cancer 17:118–126, 2007

B. Scholkopf, S. Kah-Kay, C. J. Burges, F. Girosi, P. Niyogi, T. Poggio, and V. Vapnik, “Comparing support vector machines with Gaussian kernels to radial basis function classifiers,” IEEE trans Signal Processing, vol. 45, pp. 2758-2765, 1997.

A. J. Smola and B. Schoelkopf, “A tutorial on support vector regression”, NeuroCOLT2 Technical Report Series NC2-TR-1998-030, ESPRIT working group on Neural and Computational Learning Theory, NeuroCOLT2, 1998.

AZ. Snyder, LE. Girton, JC. Morris, and RL. Buckner. Normative estimates of cross-sectional and longitudinal brain volume decline in aging and AD. Neurology, 64: 1032-1039,2009.

S. Theodoridis and K. Koutroumbas, “Pattern Recognition”, Academic Press, San Diego, 1999.

Thompson PM, Hayashi KM, Dutton RA, Chiang MC, Leow AD, Sowell ER, De Zubicaray G, Becker JT, Lopez OL, Aizenstein HJ, Toga AW(2007) Tracking Alzheimer‟s disease. Ann N Y Acad Sci 1097, 183-214.

V. N. Vapnik. The Nature of Statistical Learning Theory. Springer-Verlag, New York, 1995.

Vemuri P, Gunter JL, Senjem ML, Whitwell JL, Kantarci K, Knopman DS, Boeve BF, Petersen RC, Jack CR, Jr. (2008) Alzheimer‟s disease diagnosis in individual subjects using structural MR images: validation studies. Neuroimage 39, 1186-1197.

V. Wan and W.M Campbell, “Support vector machines for speaker verification and identification,” in Proc. IEEE Workshop Neural Networks for Signal Processing, Sydney, Australia, pp. 775-784, 2000.

R. Kiran, S. R. Jetti, and G. K. Venayagamoorthy, „Online training of generalized neuron with particle swarm optimization‟, in International Joint Conference on Neural Networks, IJCNN 06, Vancouver, BC, Canada, pp. 5088– 5095. Institute of Electrical and Electronics Engineers, (2006).

E. Ceyhan, C. Ceritoglu, and et al. Analysis of metric distances and volumes of hippocampi indicates different morphometric changes over time in dementia of alzheimer type and nondemented subjects. Technical Report, Department of Mathematics, Koc University, Istanbull, Turkey, 2008.

M. Trosset, C. Priebe, Y. Park, and M. Miller. Semisupervised learning from dissimilarity data. Technical Report Department of Statistics, Indiana University, Bloomington, IN4705, 2007.

Yu O, Mauss Y, Zollner G, Namer IJ, Chambron J: Distinct patterns of active and non-active plaques using texture analysis on brain NMR images in multiple sclerosis patients:preliminary results. Magn Reson Imaging 17:1261–1267, 1999


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