ABSTRACT Automatic diagnosis may help to decrease human based diagnosis error and assist physicians to focus on the correct disease and its treatment and to avoid wasting time on diagnosis. In this paper computer aided diagnosis is applied to the brain CT image processing. We compared performance of morphological operations in extracting three types of features, i.e. gray scale, symmetry and texture. Some classifiers were applied to classify normal and abnormal brain CT images. It showed that morphological operations can improve the result of accuracy. Moreover SVM classifier showed better result than other classifiers.
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nullFallahi, A. , Pooyan, M. and Khotanlou, H. (2010) A new approach for classification of human brain CT images based on morphological operations. Journal of Biomedical Science and Engineering, 3, 78-82. doi: 10.4236/jbise.2010.31011.
 Zhang, W.L. and Wang, X.Z. (2007) Extraction and classification for human brain CT images. Proceedings of Sixth International Conference on Machine learning and cybernetics, IEEE., 1155-1156.
Stoitsis, J., Valavanis, I., Valavanis, S.G., Golemati, S., Nikita, A. and Nikita K.S. (2006) Computer aided diag- nosis based on medical image processing and artificial intelligence methods, Nuclear Instruments and Methods in Physics Research, 569, 591-595.
Haruka, D. and Teruak, A. (2007) Characterization of spatiotemporal stress distribution during food fracture by image texture analysis methods. Journal of Food Engineering, 81, 429-436.
Wang, X.Z. and Lin, W.X. (2007) Application of induc- tive learning in human brain CT image recognition. Proceedings of Sixth International Conference on Machine learning and cybernetics, IEEE., 1155-1156.
Haralick, R.M., Shanmugam, K. and Dinstein, I. (1973) Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3, 6, 610-621
Haruka, D. and Teruak, A. (2007) Characterization of spatiotemporal stress distribution during food fracture by image texture analysis methods, Journal of Food Engin- eering, 81, 429-436.
Giardina, C.R. and Dougherty, E.R. (1988) Morpholo- gical Methods in Image and Signal Processing. Engle- wood Cliffs, NJ, Prentice–Hal.
Chen, C.W., Luo, J. and Parker, K.J. (1998) Segmen- tation via adaptive K-Mean clustering and knowledge- based morfological operations with biomedical applica- tions, IEEE Trans, Image Processing, Vol. 7, 12, 1673- 1683.
Mitchell, T.M.(2003) Machin learning. China Machine Press, Beijing.
Mandayam, S. and Policar, R. Artificial neural networks. Lecture Note.
Duin, R.P.W., Juszczak, P., Paclik, P., Pekalska, E., de Ridder, D. and Tax, D.M.J. (2004) A matlab toolbox for pattern recognition. PRTools4, Delft University of Technology.