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The manual interpretation of MRI slices based on visual examination by radiologist/physician may lead to missing diagnosis when a large number of MRJs arc analyzed. To avoid the human error, an automated intelligent classification system is proposed. This research paper proposes an intelligent classification technique to the problem of classifying four types of brain abnormalities viz. Metastases, Meningiomas, Gliomas, and Astrocytomas. The abnormalities are classified based on Two/Three/ Four class classification using statistical and texlural features. In this work, classification techniques based on Least Squares Support Vector Machine (LS-SVM) using textural features computed from the MR images of patient are developed. LS-SVM classifier using non-linear radial basis function (RBF) kernels is compared with other techniques such as SVM classifier and K-Ncarest Neighbor (K-NN) classifier. It has been observed that the method proposed using LS-SVM classifier outperforms all the other classifiers tested.
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Tom
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89--103
Opis fizyczny
Bibliogr. 18 poz.
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autor
autor
autor
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- Anna University, MIT Campus, Chennai, India
Bibliografia
- [1] WEBSTER J. G. (ed.), Medical Instrumentation: Application and Design. John Wiley & Sons, Inc., New York, 1998, 551-555.
- [2] HAACK E. et al., Magnetic Resonance Imaging, Physical- Principles and Sequence Design, Wieley-Liss, New York 1999.
- [3] LERSKI R. A., STRAUGHAN K., SCHAD L. R., BOYCE D., BLUML S., ZLINA I., MR Image Texture Analysis — An Approach to Tissue Characterization, Magnetic Resonance Imaging, Vol. 11, 1993, 873-887.
- [4] SCHAD L. R., BLUML S., ZUNA I., MR Tissue Characterization of Intracranial Tumors by Means of Texture Analysis, Magnetic Resonance Imaging, Vol. 11, 1993, 889-896.
- [5] FREEBOROUGH P. A., Fox N. C., MR Image Analysis to the Diagnosis and Tracking of Alzheimer’s Disease, IEEE Transaction on Medical Imaging, Vol. 17, No. 13, June 1998, 475-479.
- [6] CAWLEY G. C., TALBOT N. L. C., Fast leave-one-out cross-validation of sparse least-squares suppon vector machines, Neural Networks, 17(10), December 2004, 1467-1475.
- [7] CHU W., ONG C. J., KEERTHI S. S., An improved conjugate gradient scheme to the solution of least squares SVM, IEEE Transactions on Neural Networks, 16(2), 2005. 498-501.
- [8] CHEN P. C., PAVLIDRI T., Segmentation by Texture Using A Co-Occurrence Matrix and a Spill And Merge Algorithm, Computer Graphics and Image Processing, 10, 1979, 172-182.
- [9] JAIN A. K., Fundamentals of Digital Image Processing, Prentice Hall of India, 1997.
- [10] HARUCK R. M., Statistical and structural approaches to texture, Proc. IEEE, Vol. 67, May 1979, 786-804.
- [11] HARLICK R. M., SHANMUGAM K., DINSTEIN I., Textural Features For Image Classification, IEEE Transaction on Systems, Man, and Cybernetics, Vol. SMC-3. No. 6. Nov. 1973, 610-621.
- [12] DUDA R. O., HART P. E.. STORK D. G.. Pattern Classification. Wiley, 2nd ed., 2001.
- [13] VAPNIK V. N., The Nature of Statistical Learning Theory, Springer-Verlag, New York 1995.
- [14] BURGES C. J. C., A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2). 1998, 121-167.
- [15] CRISTIANINI N., SHAWE-TAYLOR J., An Introduction to Support Vector Machines, Cambridge Univ. Press, Cambridge, U.K., 2000.
- [16] HSU C. W., LIN C. J., A comparison of methods for multi-class support vector machines, IEEE Trans, on Neural Network, Vol. 13, No. 2, Mar. 2002, 415-425.
- [17] SUYKENS J. A. K., VANDEWALLE J., Least Squares Support Vector Machine Classifiers. Neur. Proc. Lett., 9(3), 1999, 293-300.
- [18] SUYKENS J. A. K., VAN GESTEL T., DE BRABANTER J., DE-; MOOR B., VANDEWALLE J., Least Squares Support Vector Machines, World Scientific Publishing Co., Singapore 2002.
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-article-BAT5-0023-0007